AMR-GNN: a multi-representation graph neural network framework to enable genomic antimicrobial resistance prediction

Domain: chemistry
Published: 2026-03-06
Editorial outcome: major_revision

Abstract
Whole-genome sequencing (WGS) data are an invaluable resource for understanding
antimicrobial resistance (AMR) mechanisms. However, WGS data are high-dimensional
and the lack of standardised genomic representations is a key barrier to AMR phenotype
prediction. To fully explore these high-resolution data, we propose AMR-GNN, a graph
deep learning-based framework that integrates multiple genomic representations with
graph neural networks (GNN) to enable AMR phenotype prediction from genomic
sequence data. We test AMR-GNN with Pseudomonas aeruginosa, a clinically relevant
Gram-negative bacterial pathogen known for its complex AMR mechanisms. We present
AMR-GNN as a proof-of-concept framework designed to address several key problems
in AMR phenotype prediction with data-driven machine learning (ML) approaches,
including using multiple genomic representations to enhance performance, to mitigate the
influence of clonal relationships and to identify informative biomarkers to provide
explainability. Follow-up validation on the largest publicly available dataset spanning both
Gram-negative and Gram-positive pathogens highlights AMR-GNN’s broad applicability
in detecting AMR in diverse and clinically relevant pathogen-drug combinations.

Introduction
Antimicrobial resistance (AMR) is a significant global health threat, leading to 1.14 million
deaths globally in 2021 [1]. Rapid and accurate detection of AMR phenotype through
antimicrobial susceptibility testing (AST) is crucial for improving patient outcomes,
reducing unnecessary antimicrobial use, and preventing the spread of AMR in hospital
settings. Culture-based methods for identifying AMR are slow, laborious and are often not
reported to the clinician for 2-5 days after sample collection [2]. These delays also impact
our ability to institute infection prevention measures to stop further AMR transmission.

Whole genome sequencing (WGS) data offer a promising alternative approach to detect
AMR. The small size of bacterial genomes makes WGS increasingly accessible to clinical
microbiology laboratories. Coupled with advances in bioinformatics, WGS data can now
support a range of analyses, including organism identification and AMR detection [3].
Additionally, the constant generation of public WGS data provides opportunities for
surveillance and enhances the robustness of bacterial studies, particularly when
statistical analyses often require large sample sizes.

For AMR detection, most pathogens have been analysed using simple rule-based and/or
gene detection approaches [4, 5]. These approaches perform well when AMR is driven
by single gene presence or mutations but are less effective in pathogens where AMR
results from complex interactions between several factors, or when genetic heterogeneity
is present (e.g. recombination or horizontal gene transfer) [3]. They also cannot discover
novel genetic variants related to AMR, potentially underutilising the high resolution of
WGS data. In recent years, machine learning (ML) has become a promising way to
overcome these limitations and has successfully predicted AMR in a wide range of
bacterial pathogens [6-9]. ML can account for complex genetic relationships, and once
trained, can detect AMR for a broad set of antimicrobials within minutes.

Given that bacterial genomes consist of sequences containing millions of nucleotides
(e.g., approximately 6 Mb for P. aeruginosa), WGS data can be represented in multiple
ways for input into ML models. However, there is currently no established consensus on
the most effective representation [4]. Using several approaches to represent bacterial
genomes provides more complete insights into the pathogen. For example, single
nucleotide polymorphisms (SNPs) help identify variants relative to a specific reference
genome, while pan-genome analysis can uncover variant genes within a population.
Despite these potential advantages, the use of multiple genome representations for AMR
phenotype prediction has remained limited. Recent computational advances (e.g. deep
learning) have inspired growing interest in multimodal learning [7, 10, 11], prompting the
question of whether this approach can effectively exploit diverse genomic inputs. We
therefore aimed to assess the impact of integrating multiple genome representations on
AMR phenotype prediction, and to leverage modern ML approaches designed for
complex, high dimensional data to further enhance prediction performance.

To address these gaps, we identified graph neural networks (GNNs) as a promising
solution. Their flexible architecture allows the integration of diverse input types through
node and edge features [12]. In contrast to other deep learning architectures, GNNs
aggregate information between samples through a similarity network. This characteristic
is particularly relevant for AMR phenotype prediction as isolates sharing similar genomic
features may share similar AST profiles [13]. While GNNs are increasingly applied in
biomedical research [12, 14], their use in bacterial genomics for AMR phenotype
prediction remains limited [15]. We therefore developed AMR-GNN, a new GNN-based
framework using multiple WGS-derived features to predict AMR phenotype in P.
aeruginosa as a use case (Fig. 1a). This pathogen has a large and diverse genome with
high genetic variability and employs a wide range of resistance mechanisms, including
efflux pumps, porin loss, and enzymatic inactivation of antibiotics [16, 17]. AMR-GNN
outperformed baseline models, including elastic net and convolutional neural network
(CNN), across a wide range of antipseudomonal agents. We further evaluated the model
on a large, diverse dataset encompassing key Gram-negative and Gram-positive
pathogens, confirming AMR-GNN’s effectiveness across multiple species and
antimicrobials.

Collectively, these results demonstrate the potential of leveraging distinct feature types
to enhance AMR phenotype prediction, establishing AMR-GNN as a robust proof-of-
concept framework for integrating these features into a unified ML architecture. Finally,
through feature attribution analysis, we highlighted the interpretability of AMR-GNN in
identifying AMR biomarkers.

Results
Data characteristics. We collected a comprehensive dataset consisting of WGS
(Illumina) and AST data from our center and nine publicly available datasets [11, 18-25].
We included datasets with minimum inhibitory concentration (MIC) values obtained from
dilution protocols, either broth microdilution (BMD) (from our centre and most public
datasets [18-25] ) or agar dilution [11]. This resulted in a complete dataset of 2,515 P.
aeruginosa isolates with diverse population structure (524 unique STs – Fig. 1b), with
ST235 (10.3%), ST174 (5.5%), ST111 (4.3%) and ST244 (4.0%) being the most prevalent.
The AST dataset consisted of 19,865 MIC entries covering 12 common antipseudomonal
antimicrobials. These values were classified as either susceptible or resistant based on
the European Committee on Antimicrobial Susceptibility Testing (EUCAST) v15.0
breakpoints [26]. The number of MIC values and resistance rates for each antimicrobial
are shown in Fig. 1c-e. We used seven datasets (our centre and [11, 18, 20-22, 24, 25])
for training and internal validation. The two smallest datasets [19, 23] were reserved as
hold-out set to assess the model’s generalisability (see Methods). Resistance rates varied
from 13.7% (amikacin) to 63.3% (ciprofloxacin) in the internal dataset (Fig. 1d), and from
11.1% (amikacin) to 79.4% (tobramycin) in the hold-out datasets (Fig. 1e).

AMR-GNN framework design. AMR-GNN predicts AMR for each isolate by treating each
isolate as a node within a graph and performing node classification. Constructing this
graph requires two key components: node features represented as feature vectors for
each isolate and edge information captured in an adjacency matrix indicating
relationships between isolates. We first evaluated AMR phenotype prediction using
individual genomic representations and selected the best-performing as the node feature
input. Following a GNN-based multi-omics integration approach [27], we used the
remaining representations to define graph connectivity via adjacency matrices (see
Methods). Each graph shared the same node features but differed in connectivity,
allowing us to learn distinct representations. To integrate information from both graphs,
we employed low-rank multimodal fusion to combine their embedding layers into a unified
representation used for final AMR phenotype prediction [28].

Models trained with a single genomic representation demonstrated a strong
baseline for AMR phenotype prediction. We evaluated models trained with different
single feature types, including unitigs, SNPs, and frequency chaos game representation
(FCGR). FCGR generates a 2𝑘 ×2𝑘 grid where k-mers are mapped according to their
nucleotide sequence and weighted by their occurrence frequency [29, 30]. In this
representation, each grid cell is analogous to a pixel in an image. For unitigs and SNPs,
we applied the elastic net, while FCGR features were used to train a CNN model (see
Methods). The complete feature sets for unitigs and SNPs contained approximately 1.5
million and 900,000 features, respectively, which were used as input into the elastic net.
For FCGR, we tested k-mer values ranging 5-8 to generate FCGR features and noted
that k=7 was optimal, with equivalent performance to k=8 but faster training time (Supp.
Fig. 1).

Models using unitig features achieved the highest area under receiver operating
characteristic curve (AUROC) values across all tested antimicrobials in the internal test
sets, showing statistically significant improvements over other feature types for 10/12
antimicrobials, excluding cefepime and ceftolozane/tazobactam (Fig. 2a, Supp. Table 1).
Unitig-based models exhibited strong predictive performance, particularly for
fluoroquinolones (ciprofloxacin AUROC 0.911 [CI: 0.904-0.918]) and aminoglycosides
(tobramycin AUROC 0.933 [CI: 0.919-0.947]). Performance for beta-lactams ranged from
AUROC 0.878 (CI: 0.847-0.910) for ceftolozane/tazobactam to AUROC 0.636 (CI: 0.603-
0.668) for cefepime. When comparing SNP and FCGR representations, FCGR-based
models significantly outperformed SNP-based models for ciprofloxacin (0.868 vs 0.733,
P<0.001), levofloxacin (0.875 vs 0.684, P<0.001), and ceftazidime (0.739 vs 0.691,
P=0.003), with comparable performance across the remaining nine antimicrobials (Supp.
Table 2). We then used the salient unitigs selected by the elastic net as node features,
while SNP and FCGR data were used to construct the adjacency matrices for AMR-GNN
(see Methods).

AMR-GNN outperformed baseline models. To evaluate the effectiveness of AMR-GNN
in improving AMR phenotype prediction in P. aeruginosa, we used AUROC as the main
metric and compared AMR-GNN with unitig-only models as the baseline (Fig. 2b, Supp.
Table 3). AMR-GNN significantly outperformed unitig-only models in 11/12 tested
antimicrobials and performed equivalently for levofloxacin (0.921 vs 0.897, P=0.104)
(Supp. Table 4). Highest AMR-GNN performance was noted for tobramycin (0.971, CI:
0.964-0.978), while lowest was for cefepime (0.819, CI: 0.783-0.855). AMR-GNN made
the most substantial improvements for the lowest performing antimicrobials in unitig-only
models (cefepime and aztreonam), achieving AUROC increases of 28.8% and 18.9%,
respectively.

Further improving performance by removing bias of bacterial population structure.
Bacterial population structure is a major confounder in AMR phenotype prediction
because of slow linkage disequilibrium decay [31]. This results in co-inheritance of causal
and non-causal variants that ML models cannot easily disentangle. As a result, models
may rely on clonal signatures (e.g., high-risk ST clone) to predict AMR instead of
uncovering the underlying resistance mechanisms. In the context of GNN, isolates within
the same MLST group form dense connections due to their high genetic similarity. This
can result in redundant graph information that can degrade performance [32]. Previous
work has demonstrated that reducing the influence of redundant neighbors such as edge
dropping or node deletion yields more robust results [32, 33]. In this study, we employed
a decoupling approach that removes all edges connecting samples from the same MLST
group, therefore forcing each node to aggregate information only from neighbors that are
not clonally related (see Methods). By using this approach, we achieved higher AUROC
for all tested antimicrobials compared to the original AMR-GNN, with significant
improvements for meropenem, amikacin, and levofloxacin (Fig. 2b, Supp. Table 5).

AMR-GNN demonstrates better generalisability on external validation than unitig-
only model. After identifying decoupled AMR-GNN as our best-performing approach, we
evaluated its performance and benchmarked against untig-only models (our prior
baseline comparator) on the hold-out test set for 8 antimicrobials. Compared to the
internal test set, AMR-GNN maintained comparable AUROC for ciprofloxacin and
ceftazidime, but showed significant performance drops for other antimicrobials (Supp.
Table 6), suggesting potential overfitting. However, AMR-GNN demonstrated greater
robustness to overfitting than unitig-only models, outperforming them in 5/8 antimicrobials
tested (Supp. Table 7).

AMR-GNN achieved superior performance compared with P. aeruginosa-specific
AMR phenotype prediction approaches. We benchmarked AMR-GNN against several
established approaches (see Methods). Rule-based methods are the current biological
standard for detecting AMR based on known AMR determinants (including resistance
genes and AMR causal variants) [4, 34]. We evaluated AMR-GNN against two specific
implementations: the method by Cortes-Lara et al. [21], (hereafter ‘rule-based’),
which
utilises the presence/absence of known AMR determinants; and ARDaP [35], an
automated scoring system combining known determinants with variants identified via
genome-wide association studies (GWAS) and ML-based feature selection. Notably,
ARDaP previously outperformed other common AMR phenotype prediction tools (e.g.,
abritAMR, AMRFinderPlus, and ResFinder) in all 10 tested antimicrobials. In addition,
although we previously demonstrated AMR-GNN’s superior performance over elastic net
and CNN models, we also compared it with VAMPr, a publicly available AMR phenotype
prediction tool that uses KEGG (Kyoto Encyclopedia of Genes and Genomes) ortholog
variants as features [36].

Based on label availability, we benchmarked AMR-GNN against a rule-based approach,
ARDaP, and VAMPr using both internal and external test sets (Fig. 3). On the internal
test set, AMR-GNN achieved universally higher F1-scores and better very major error
rates (VME) across all tested antimicrobials (n=5, 8, and 8, respectively). In the external
validation (comprising 5, 6, and 6 antimicrobials), AMR-GNN demonstrated superior F1-
scores in 4/5, 3/6, and 4/6 cases, respectively. For VME, AMR-GNN achieved better
results for 5/5, 4/6, and 4/6 cases, respectively. AMR-GNN underperformed other
approaches for meropenem resistance prediction (F1: 0.562, CI: 0.478-0.647). We noted
meropenem resistance was significantly more prevalent in the hold-out data than in the
internal set (54.0% vs 31.4%; P<0.001, Chi-square test), which likely contributed to this
performance drop.

AMR-GNN demonstrated robust predictive performance across several key
bacterial pathogens. To assess the efficacy of AMR beyond P. aeruginosa, we
evaluated its performance using extensive datasets from two Gram-negative organisms
[Escherichia coli [n=10,246] and Klebsiella pneumoniae [n=7,072]] and two Gram-positive
organisms [Staphylococcus aureus [n=3,195] and Enterococcus faecium [n=2,608]]. The
analysis covered a comprehensive panel of antimicrobials specific to each species,
sourced from The Bacterial and Viral Bioinformatics Resource Center (BV-BRC)
databases [37, 38] (Fig. 4a-d; see Methods). AMR-GNN consistently achieved high
predictive performance, with mean AUROCs exceeding 0.9 for nearly all tested
antimicrobials, except for piperacillin/tazobactam in E. coli (0.871, CI: 0.861-0.881) (Fig.
4e-h). For each species, highest AUROCs were noted for ceftriaxone in both E. coli (0.988;
CI: 0.986-0.989) and K. pneumoniae (0.985, CI: 0.981-0.990), fusidic acid in S. aureus
(0.991, CI: 0.988-0.994); and vancomycin in E. faecium (0.995, CI: 0.993-0.997). Clinical
decision metrics were similarly strong: sensitivity/specificity of 0.938/0.968 for E. coli-
ceftriaxone, 0.853/0.986 for K. pneumoniae-ceftriaxone, 0.990/0.909 for S. aureus-fusidic
acid, and 0.934/0.992 for E. faecium-vancomycin (Supp. Table 8). VME values ranged
from 0.008 to 0.133, while ME values ranged from 0.054 to 0.336. In terms of
benchmarking, we compared the performance of AMR-GNN with ResFinder, a tool that
has shown strong results relative to several other AMR phenotype prediction methods [5].
Specifically, we assessed predictions across 16 species-antimicrobial combinations. In
terms of F1-score, AMR-GNN outperformed ResFinder in 14 of these 16 comparisons.
The only exceptions were tobramycin in E. coli (0.834 vs. 0.859) and fusidic acid in S.
aureus (0.929 vs. 0.969) (Fig. 4i). The most significant performance improvement with
AMR-GNN was observed for linezolid in E. faecium (0.859 vs 0.512).

Model interpretation on best-performing antimicrobials. We employed integrated
gradient (IG) analysis to identify the genetic features contributing most to AMR-GNN
performance [39]. We selected the highest-performing antimicrobials from three key
antipseudomonal classes: fluoroquinolones (levofloxacin), aminoglycosides (tobramycin),
and beta-lactams (ceftolozane/tazobactam) (Table 1) [39]. For levofloxacin, the model a
priori identified known target sites (gyrA, gyrB, and parC) as the most important
contributors [40], while PA4179 encodes a probable OpdK porin allowing antimicrobial
transport across the bacterial membrane [41, 42]. For tobramycin and
ceftolozane/tazobactam, most shortlisted genes had undetermined functions. fusA1
(tobramycin) has been shown to lead to downstream modification that result in a 4- to 8-
fold increase in the MIC for several aminoglycosides, including tobramycin [43]. For
ceftolozane/tazobactam, PA4520 is located immediately upstream of ampE and ampD,
which may allow over-expression of AmpC beta-lactamases. We further validated model
interpretation results by assessing impact of mutations in shortlisted genes of known
functions. Compared to wild-type isolates, MICs were significantly higher in isolates with
mutations: gyrA (P=2.655 x 10-134), gyrB (P=6.959 x 10-5), parC (P=1.053 x 10-103) in
levofloxacin, and fusA1 (P= 1.452 x 10-4) in tobramycin (Mann-Whitney U-test) (Supp.
Fig. 2a,b). In terms of specific mutations, we identified 9, 2, and 4 mutations in gyrA, gyrB,
and parC, respectively, that were significantly enriched in levofloxacin-resistant isolates
(Chi-square test; Supp. Table 9). Additionally, 7 mutations in fusA1 were significantly
associated with tobramycin resistance (Supp. Table 9).

Methods
Data collection. This study was approved by the Alfred Hospital Ethics Committee
(Project No. 185/21). We collected short-read (Illumina) genomic data and broth
microdilution (BMD) results (Sensititre™ Gram-negative plate, Thermo Fisher; plate code:
DKMNG) for 341 P. aeruginosa isolates previously obtained at Alfred Hospital between
2004 and 2021. As these data were sourced from a biorepository (Alfred Hospital Ethics
Committee Project No. 533.16), patient consent was waived. Additionally, we collected
data for 2,174 isolates from nine publicly available datasets (NCBI project accession
numbers: PRJEB21341, PRJEB31047, PRJEB37711, PRJEB40140, PRJEB61879,
PRJNA264310, PRJNA288601, PRJNA526797, and PRJNA656645) [11, 18-25].
Together, this accounts for the 2,515 P. aeruginosa isolates included in the analysis. We
used sra-tools v3.0.0 (https://github.com/ncbi/sra-tools) to download the public
sequencing read sets. We converted MIC values to binary label (susceptible/resistant)
using the EUCAST v15.0 breakpoint tables [26].

Training, testing and hold-out dataset generation. First, for external validation, we
identified two collections with the smallest sample sizes (42 and 21 isolates) and set them
aside as a hold-out set (referred to as “external test set”) [19, 23]. The hold-out dataset
contained AST results for nine antimicrobials; however, we excluded imipenem from the
external validation due to absolute resistance rate (100%). Consequently, we conducted
external validation for eight antimicrobials: meropenem, tobramycin, amikacin,
ciprofloxacin, ceftolozane/tazobactam, ceftazidime, piperacillin/tazobactam, and
aztreonam. We split the remaining data into training and testing sets using an 80/20
stratified split based on AST labels for each antimicrobial. This test set is referred to as
internal test set. To account for performance variability, this process was repeated 10
times with random seeds. For consistency, the same training/testing splits were used
across all experiments, including models based on single genomic representations, AMR-
GNN training, and benchmarking with external AMR phenotype prediction tools. For
AMR-GNN and the CNN model using FCGR inputs, the training set was further divided
into training (80%) and validation (20%) subsets. These models were trained on the
reduced training set to minimise validation loss. The final models were evaluated on both
internal and external test sets.

WGS processing. We used Unicycler v0.5.0 to assemble short-read WGS data into
contigs [53]. The assemblies were then uploaded to Pathogenwatch
(https://pathogen.watch) to filter out contaminated isolates, ensuring that only P.
aeruginosa isolates were included in the analysis, and to obtain MLST results. For unitig
input, we generated a unitig database using unitig-caller v1.2.1 [54]. To reduce memory
requirements associated with large unitig sets, we filtered out unitigs that were either too
rare (present in less than 5% of isolates) or too common (present in more than 95%). This
filtering reduced the total number of unitigs from 5,590,988 to 1,537,357. Variant calling
was performed using Snippy v4.4.5 (https://github.com/tseemann/snippy) which
generated a SNP profile, including single nucleotide polymorphisms and indels, for each
isolate using the PAO1 reference genome (Genbank: NC_002516.1). The individual
variant call format (VCF) files were then merged into a single VCF input file using
BCFtools v1.20 for the prediction step with pyseer v1.3.10 [55]. FCGR was used to
encode the frequency of k-mers into an image-like representation [30]. Following the
approach described by Green et al. for Mycobacterium tuberculosis [6], we extracted
sequences of 57 genes known to be associated with antimicrobial resistance (AMR) in P.
aeruginosa. The curated gene list can be found in Supp. Table 10 [16, 21] . The gene
sequences were aligned to the PAO1 genome and extracted from the Snippy output.
Each gene sequence was then converted into an FCGR matrix, and all matrices were
stacked along a new axis. This process resulted in an image-like input with dimensions
(C, H, W), where C represents the channel dimension (i.e., the number of AMR genes,
n=57), and H=W=k2, with k being the size of the k-mer used to extract FCGR features.
We tested k values ranging from 5 to 8 and determined that 7-mer was the optimal k-mer
setting.

Machine learning model development for single genomic representation. We trained
models to predict AMR using single genomic representations and selected the best-
performing representation as our baseline benchmark. Here, we extracted multiple WGS-
derived features, including unitig, SNP and FCGR [30]. Such genomic representations
have each demonstrated efficacy in AMR phenotype prediction and potentially provide
different strengths in representing bacterial genomic information [9, 56, 57]. For example,
SNPs identify changes in single positions [11] but depend on comparison with a reference
genome, while unitigs offer an unbiased, non-reference approach [56, 58]. On the other
hand, image-like representations such as FCGR may be more suitable for deep learning
models [6, 9, 57]. For unitig and SNP features, we used pyseer's implementation (v1.3.10)
of the elastic net model with the flags --wg enet and --alpha 1 [59]. For FCGR
representation, we used the same CNN architecture as described by Ren et al. [9],
consisting of 3 convolutional layers and a final classification head. To compute the loss,
we used binary cross-entropy function (BCELoss) adjusted by class-specific weights. We
employed mini-batch gradient descent with a batch size of 32, which is regarded as a
robust empirical baseline [60, 61]. We trained our models for 200 epochs. We used the
Adam optimizer with a multi-step learning rate, starting from 10-3 and reducing to 10-4 and
10-5 after the 50th and 100th epochs, respectively. The best performing model is the one
minimizing the validation loss. We used the following packages to develop our models:
PyTorch v2.2.0, PyTorch Lightning v2.1.3, scikit-learn v1.4.0, and Weights & Biases
(wandb) v0.16.3.

Model evaluation and statistical analysis. We validated our models with 10-fold
random splits to ensure robustness. We selected AUROC as our primary metric to assess
prediction performance. If models only predict binary AMR phenotype, we evaluated F1-
score. Additionally, we reported the area under the precision-recall curve (AUPRC),
sensitivity, specificity, and log-loss. The classification threshold was chosen to maximize
the F1-score. In addition, we also reported clinically related metrics, i.e., VME and ME:
𝑅𝑒𝑠𝑖𝑠𝑡𝑎𝑛𝑡 𝑖𝑠𝑜𝑙𝑎𝑡𝑒𝑠 𝑓𝑎𝑙𝑠𝑒𝑙𝑦 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 𝑎𝑠 𝑠𝑢𝑠𝑐𝑒𝑝𝑡𝑖𝑏𝑙𝑒
𝑉𝑒𝑟𝑦 𝑚𝑎𝑗𝑜𝑟 𝑒𝑟𝑟𝑜𝑟 𝑟𝑎𝑡𝑒 (𝑉𝑀𝐸) = (1)
𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑟𝑒𝑠𝑖𝑠𝑡𝑎𝑛𝑡 𝑖𝑠𝑜𝑙𝑎𝑡𝑒𝑠
𝑆𝑢𝑠𝑐𝑒𝑝𝑡𝑖𝑏𝑙𝑒 𝑖𝑠𝑜𝑙𝑎𝑡𝑒𝑠 𝑓𝑎𝑙𝑠𝑒𝑙𝑦 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 𝑎𝑠 𝑟𝑒𝑠𝑖𝑠𝑡𝑎𝑛𝑡
𝑀𝑎𝑗𝑜𝑟 𝑒𝑟𝑟𝑜𝑟 𝑟𝑎𝑡𝑒 (𝑉𝑀𝐸) = (2)
𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑠𝑢𝑠𝑐𝑒𝑝𝑡𝑖𝑏𝑙𝑒 𝑖𝑠𝑜𝑙𝑎𝑡𝑒𝑠
The optimal threshold was determined by minimising the weighted sum of VME and ME:
𝑂𝑝𝑡𝑖𝑚𝑎𝑙 𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑 = argmin(𝜆𝑉𝑀𝐸 +𝑀𝐸) (3)
Given that the Food and Drug Administration (FDA) establishes stricter acceptable
thresholds for VME (1.5%) compared to ME (3%) for new AST systems [62], we applied
a weighting factor of 𝜆=2 to prioritise VME minimisation. All metrics were reported as
mean values with 95% confidence intervals (95% CI). Unless otherwise specified, we
used the Mann-Whitney U test to compare performance between two different feature
types or model architectures. A significance level of P=0.05 was applied.

AMR-GNN development. We employed graph convolutional neural network (GCN) for
multi-genomic representation learning [63]. The graph network requires two input types:
a feature vector for each node and an adjacency matrix that defines the bi-directional
edges connecting pairs of nodes. For each antimicrobial, we constructed the node
features using the unitigs selected by elastic net model. Specifically, we retained all
unitigs with non-zero beta coefficients. The feature data was represented as a binary
feature vector, where a value of 0 indicated the absence of the corresponding unitig in
the genome, while a value of 1 denoted its presence.

For edge construction, we first created distance matrices for both SNP and FCGR
representations using Hamming code and Euclidean distance, respectively. Next, we
arranged the unique distances in ascending order. Considering n as the total number of
samples and k‰ as the desired proportion of edges to be retained in the final graph, we
calculated the total number of possible undirected edges (|E| ) and the number of edges
total
to be kept (|E| ) using the following equations:
kept
𝑛 × (𝑛−1)
|𝐸| = (4)
total
2
|𝐸| = int(
|𝐸|𝑡𝑜𝑡𝑎𝑙 × 𝑘
) (5)
kept
1000
For each distance matrix, we set the distance threshold to determine the adjacency matrix.
We incrementally increased the distance threshold, connecting two nodes if their distance
was less than the current threshold. At each step, we calculated the total number of edges
in the resulting adjacency matrix (|E| ). We continued this process until the number of
edges exceeded a predefined value |E| > |E| kept. The adjacency matrix obtained from the
previous threshold was then selected as the final adjacency matrix. This matrix was
converted into the COO (Coordinate list) format, which is compatible with pytorch-
geometric’s data object (v2.4.0). For the hyperparameter k, we gradually increased the
threshold until k=20, as the performance deteriorated significantly beyond that for all
antimicrobials. We found that k=2 provided the best results (Supp. Fig. 3).

In terms of model architecture, we observed that adjacency matrices derived from
different genomic representations varied, particularly at lower retention thresholds (k)
(Supp. Fig. 4), suggesting that each graph might capture distinct aspects of the data. We
therefore developed a dual GCN model that integrates the representation layers from two
separate GCN modules into a unified vector input (Figure 2). This combined vector
serves as the input to predict AMR. Each GCN module learns from the unitig node
features and an adjacency matrix, which is inferred from either SNP or FCGR feature.
The latent representation ℎ , obtained from each GCN module, can be expressed as
𝑖
follows:
ℎ = GCN(𝑋,𝐴 ) (6)
𝑖 𝑖
where 𝑖 ∈ [𝑆𝑁𝑃, 𝐹𝐶𝐺𝑅] , 𝑋 and 𝐴 are the node feature and adjacency matrix,
𝑖
respectively. Specifically, each GCN module consists of two GCN layers, each containing
128 units with rectified linear units (ReLU) activation. We applied layer normalisation and
used a dropout rate of 0.5. This generated two 128-dimensional representation vectors
prior to the classification head. In literature, various approaches have been explored to
combine multiple representations. Among these techniques, tensor presentation has
demonstrated superior performance compared to simple fusion methods such as
concatenation or pooling. However, such an approach often increases the computational
resource exponentially with respect to the number of data modalities. To address this
challenge, we adopted the low-rank fusion technique, a tensor representation approach
introduced by Liu et al. [28]. This method allows us to efficiently combine the learned
representations from two graph networks into the final layer preceding the classification
head while maintaining computational efficiency. We used PyTorch Geometric v2.4.0 to
develop our AMR-GNN model.

Clonal relationship decoupling process. We first used MLST information to construct
a MLST matrix (M) similar to an adjacency matrix, where M = 1 if the ith isolate from the
i,j
row and the jth isolate from the column are in the same MLST group, and M = 0
i,j
otherwise. We then used the M matrix to update the adjacency matrix (A) such that A =
i,j
0 if M = 1, resulting in the final decoupled adjacency matrix.

AMR phenotype prediction with rule-based approaches. As no pipeline was provided
for the approach by Cortes-Lara et al. [21], we developed our own workflow to classify
isolates as susceptible or resistant to five antimicrobials: meropenem, tobramycin,
ciprofloxacin, ceftazidime, and ceftolozane/tazobactam. For each antimicrobial, we
evaluated the known AMR determinants and assigned scores based on a reference table.
Isolates with a cumulative score of 1 or higher were categorized as resistant, while those
with scores below 1 were classified as susceptible. We utilized the variant calling results
obtained from Snippy to assess chromosomal mutations and queried the genes of interest
using the gene annotation results from bakta to determine gene presence or absence [64].
We excluded all natural polymorphism variants, as defined by Cortes-Lara et al., when
calculating the scores. Regarding ARDaP [35], we ran the Nextflow pipeline
(https://github.com/dsarov/ARDaP) to generate the report and extracted the predicted
phenotypes for the tested antimicrobials.

AMR phenotype prediction with VAMPr. We performed command-line VAMPr AMR
phenotype prediction [36], using the approach described
(https://github.com/jiwoongbio/VAMPr). AMR phenotype prediction models of amikacin,
aztreonam, cefepime, ceftazidime, imipenem, piperacillin/tazobactam, and tobramycin
can be found at https://cdc.biohpc.swmed.edu/VAMPr/VAMPr.cgi.

AMR-GNN validation on BV-BRC database. Assemblies of tested isolates were
downloaded from the FTP server at ftp://ftp.bvbrc.org [37, 38]. For Gram-negative
pathogens (E. coli and K. pneumoniae), we evaluated susceptibility to cefepime,
ceftriaxone, piperacillin/tazobactam, meropenem, and tobramycin. For E. faecium,
vancomycin and linezolid were tested, whereas for S. aureus, we included oxacillin,
fusidic acid, ciprofloxacin, clindamycin, daptomycin, rifampicin, and
trimethoprim/sulfamethoxazole. To standardize susceptibility labels across datasets,
isolates with available MIC values were reclassified according to EUCAST v15 clinical
breakpoints [26]. For those having only AST labels, samples labeled as intermediate ("I")
were excluded from analysis as it was unclear which interpretive criteria were used. For
each organism-antimicrobial combination, we followed the same approach used
previously for P. aeruginosa: datasets were split into training and testing subsets, and
models were trained across 10 random splits. During feature (unitig) selection for E. coli,
the training dataset was limited to 3,000 isolates due to memory constraints associated
with large variant files. Reference genomes used for variant calling were: E. coli str. K-12
substr. MG1655 (Genbank: U00096.3), K. pneumoniae subsp. pneumoniae MGH 78578
strain ATCC (Genbank: CP000647.1), E. faecium strain SRR24 chromosome (Genbank:
CP038996.1), and S. aureus subsp. aureus NCTC 8325 (Genbank: CP000253.1). Known
resistomes for FCGR feature extraction were derived from the curated Antibiotic
Resistance Ontology (ARO) gene list from the Comprehensive Antibiotic Resistance
Database (CARD) (Supp. Table 11-14) [65].

Model interpretation. We assessed node feature importance using IG [39]. IG adheres to two key interpretability
axioms: (1) sensitivity, ensuring identical attributions for models with identical outputs;
and (2) completeness stating that the total attributions equal the output difference
between the models using actual input and baseline input (e.g., a zero input). Here, we
used Captum’s implementation (v0.7.0) of IG to compute the attribution scores [66].
To link salient node features (unitigs) to biological meaning, we performed BLAST
searches to identify corresponding genes [67]. The top 10 genes were prioritized based
on the cumulative IG scores of their mapped unitigs. We then used decision plots derived
from SHAP (Shapley Additive Explanation) for interpretation [68].

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Summary:\nThe authors have developed a novel AMR phenotype detection tool called AMR-GNN. AMR-GNN utilises a graphical neural\nnetwork to predict AMR phenotype from several genomic inputs: unitigs, SNPs, and frequency chaos game representation.\nThe authors originally used 2,515 Pseudomonas aeruginosa genomes with corresponding MIC values to 12\nantipseudomonal antimicrobials (from broth dilution or agar dilution) to test/train the models. The isolates are from varied\nsequence types to have a diverse population. They used EUCAST breakpoints to classify their isolates into a binary\nsusceptible or non-susceptible. They trained the models with 80% of the data (~1962 isolates), tested with 20% of the data\n(~490 isolates), and then used an external validation set of 63 isolates to further test the model predictions. The baseline\nmodel using only unitigs is not as good as the complete AMR-GNN model. For example, there is a >28% increase for\ncefepime when using AMR-GNN. The authors benchmarked their tool against three other AMR prediction methods, and I am\nnot familiar with these methods (see overall comments). I liked that the authors made additional models of key species from\nboth Gram-positive and Gram-negative bacteria. These models also had high predictive accuracy, showing their method is\nnot limited to one species. The authors were able to identify some of the key genes which were making large contributions to\nthe model performance. Many of the genes contributing to the model performance were already known in the AMR\ndatabases. They do mention this briefly, but in some sections, this is not clear. They could use this to show that their models\nare drawing out the correct key genes of interest. However, the authors noted some genes which are not well known and\nhighlighted clearly how they could be involved in contributing to resistance. Overall, I found the paper to be clear and well-\nwritten. I found it easy to follow, and the logic between the sections was clear to me. The figures were clear and easy to\ninterpret overall. I wonder if Fig.2b would be easier to visualise in a way that the models for each drug would be easier to\ncompare, for example, a bar chart for each measurement (e.g. F1). However, this could be personal preference. The\nconclusions lack some thoughts about the significance of the software. For example, identifying possible key genes involved\nin AMR that are unknown is critical to understanding AMR.\nOverall comments:\n- The authors often say, “AMR prediction”. I think it would be clearer to say AMR phenotype prediction (so it is not confused\nwith genotype)\n- Line 59: The authors mention how WGS approaches will “streamline” the process of AMR detection, but organisms would\nstill have to be cultured to be able to do WGS?\n- Lines 117- 118: broth microdilution and agar dilution are very different approaches? The same isolate could have different\nAMR phenotypes using these two different models. How do you think this will impact the model?\n- Line 123-126: I don’t really understand why you selected the external validation data from two small datasets rather than\npooling the data and randomly selecting the data. This could have avoided some of the phenotype imbalances.\n- Some of the datasets are quite imbalanced, which could cause issues with overfitting.\n- Line 146: Would it be possible to give a little bit more detail of what a FCGR is?\n- Lines 308-309: Do you think this drop in prediction accuracy could be due to the models being overfit, or that they did not\nhave all the data required to train a model for better predictions?\n- I find the benchmarks a bit limited. I am not familiar with any of the benchmark methods; therefore, I think the phrase “state-\nof-the-art rule-based” is exaggerated, possibly? The authors do not mention tools such as ResFinder, which is much more\nhighly cited. Do the others not mention these types of tools because they are less complex (i.e. not a neural network model?)\nor for a different reason?\n- Many of the tools do not have version numbers, which are necessary for reproducibility.\n- The GitHub for AMR-GNN lacks information about what the model uses to predict and what outputs are expected. I ran the\ndemo data, and the code worked to my knowledge (the Conda command did not work for me and required a bit of tweaking\nbefore I could install the software). I have previously built different types of neural networks, so I am familiar with the\nterminology. However, the output files do not have enough information for someone not familiar with neural networks. I\nwould suggest that the authors provide a summary output file and explain this in the README.md file. The GitHub should\nexplain in detail what kind of input is expected and how the user can prepare their data to be in the correct format. A help\nmenu, either on GitHub or on the command line, would be helpful.\n- The paper also explained how the models are biologically interpretable. Could this be explained in the GitHub on how I\ncan interpret the results this way, or is this not currently added into this version?\n- Line 441: The authors mention a batch size of 32 for the building of their model. This may be a bit small when training with\n>2000 genomes. I would like the authors to explain why this is an appropriate method, in case I have misunderstood\nsomething.\nLine-by-line issues:\n- Line 51: “detection of AMR” could be made clearer by saying “detection of AMR phenotype”\n- Line 54: “Our current methods for identifying”   why not say “Culture-based methods…” to avoid confusion of when people\nuse WGS techniques?\n- Line 102: “multiple genome representations” possibly is not the best way to phrase it?\n- Line 121: I assume that there are not 12 MIC values for each isolate; it would be nice to say how many MIC values there\nare in total\n- Line 122: “non-sensitive” is used to describe resistant isolates; why not just say “resistant” to avoid confusion about\n“intermediate” phenotypes being included with non-sensitive?\n- Line 148: Why not mention the feature selection here?\n- Line 152: Define the acronym “AUROC”\n- Lines 180-183: The sentence starting with “Bacteria” is quite a long sentence.\n- Line 225: The numbers relating to model performance are confusing.\n- Line 239: ‘PATRIC’ is now called BV-BRC\n- Lines: 368-369: The authors talk about applying their models to “multi-omics data”, would this be possible if they are\ntaxonomically specific? Or will the models eventually be more flexible this way?", "reviewer_2": "The authors introduce AMR-GNN, a dual-graph GNN that uses unitigs as node features and builds two similarity graphs\n(from SNP and FCGR representations) and fuses them via low-rank multimodal fusion. A simple but effective “decoupling”\nstep drops edges between isolates of the same MLST to blunt clonal-structure bias.\nThe manuscript is well written, the figures are of top quality, and the supplementary files contain all required information\n(MIC values and sequence accession IDs). I particularly appreciated the stepwise testing of the algorithm in this study\n(single features vs full algorithm, with and without decoupling), which makes it clear how each component contributes to\noverall performance.\nOverall, this is an excellent and solid paper that will be of interest to the readership of Nature Communications. I recommend\nits acceptance for publication if the authors can address the following concerns:\nMajor concerns\n_ benchmarks on other bacterial pathogens: it is impossible to assess the robustness of AMR-GNN performance without\nreference points (i.e., results obtained from other tools). I would kindly ask the authors to run VAMPr or ARIBA, or any AMR\nphenotype prediction tool, on these four datasets.\n_ content of the discussion section: while I found the overall manuscript very interesting, I’m afraid the Discussion section\nwas particularly poor. The first five paragraphs consist of a long, and perhaps unnecessary, recapitulation of the results. The\ntwo paragraphs that follow mention limitations of the study (see below), but there is no real discussion of the analyses. I\nwould have liked some discussion of, for instance, the contribution of each feature type to performance, given their different\nnature, and which additional ones could be added in the future. Also, how do the authors know that ST is the optimal\ndistance level to remove population effects?\n_ applicability of the algorithm: It isn’t clear to me after reading the manuscript whether AMR-GNN represents a preliminary\nattempt at integrating different sources of features or a ready-to-use tool that could be deployed for large-scale AMR\ninferences. The authors mention two limitations regarding its applicability: the need to rebuild the “database” (I’m afraid I did\nnot understand this part) and its “computational complexity” (are the authors referring to runtime and memory usage?).\nHowever, I believe these aspects should be mentioned in the abstract and introduction to let the reader fully understand the\ncurrent applicability of AMR-GNN.\nMinor concerns\n_ the GitHub repo could benefit from more detailed instructions regarding AMR-GNN usage. There is currently no manual.\n_ supplementary file 1 is a PDF consisting of two tables spanning 270 pages. Could the authors provide these data as an\nExcel file, which would be easier to read and extract data from?\n_ Introduction, page 4: the “high-dimensional nature” of genomic data is not sufficiently explained in the introduction. I\npersonally thought the authors were referring to higher-order features (e.g., impact of variants on promoter activity or protein\nstability).\n_ l. 100–101: “their use in bacterial genomics for AMR prediction remains limited.” If GNNs have already been used for AMR\nprediction, references are needed. If not, the sentence might need slight rewording.\n_ l. 286–287: the model does not make any predictions per se of horizontally transferred AMR genes. Please reword.\n_ l. 377: please define the abbreviation “BMD.”\n_ l. 377: please also indicate here how many of these isolates were collected in this study (I believe the number is 341).\n_ l. 390: eight, not seven.\n_ l. 400: please clarify what “internal” means. If it corresponds to the unpublished set of isolates, then “remaining data” in l.\n392 should be modified into “remaining published data.”\n_ l. 439: consisting “of”?", "reviewer_3": "This manuscript proposes AMR-GNN, a graph deep learning–based framework that integrates multiple genomic\nrepresentations with graph neural networks (GNN) to predict antimicrobial resistance (AMR) from genomic sequence data of\nPseudomonas aeruginosa. Although Figure 1 illustrates that the features are mainly derived from unitigs, SNPs, and FCGR,\nthe description in the Methods section and the demo dataset provided at https://github.com/andyvng/amr-gnn suggest that\nAMR prediction does not directly start from genomic assemblies, but rather requires an adjacency matrix as input. However,\nthe information on the website appears insufficient to clarify how genomic assemblies can be practically input to obtain AMR\npredictions. Additional methodological transparency would therefore be beneficial.\nFurthermore, while the study employs GNN to evaluate performance using metrics such as F1, AUROC, sensitivity, and\nspecificity, it overlooks what is arguably the most clinically critical metric in AMR prediction: the very major error (VME)\nrate. In addition, the reported F1 scores (Figure 3) are approximately 0.8, which is lower than those achieved by prior studies\n(e.g., https://pmc.ncbi.nlm.nih.gov/articles/PMC8575023/), where F1 values around 0.92 were reported (link to model stats).\nAs a result, it is not entirely clear whether this GNN-based approach offers superior performance compared to existing\nmethods."}, "author_response": "Dear Reviewers,\nThank you for your careful consideration of our manuscript ‘AMR-GNN: A multi-\nrepresentation graph neural network framework to enable genomic antimicrobial\nresistance prediction’. Your comments are much appreciated, and we believe your\ninput has strengthened the manuscript. Please find our point-by-point response to your\ncomments below. All line numbers refer to the manuscript with tracked changes\n(Simple Markup mode).\nREVIEWER #1 (Remarks to the Author)\nComment 1.1:\n- The authors often say, “AMR prediction”. I think it would be clearer to say AMR\nphenotype prediction (so it is not confused with genotype)\nResponse 1.1:\nWe have replaced “AMR prediction” with “AMR phenotype prediction” as suggested.\nComment 1.2:\n- Line 59: The authors mention how WGS approaches will “streamline” the process of\nAMR detection, but organisms would still have to be cultured to be able to do WGS?\nResponse 1.2:\nWe have revised the manuscript accordingly.\nLine 61 reads:\n“Coupled with advances in bioinformatics, WGS data can now support a range of\nanalyses, including organism identification and AMR detection.”\nComment 1.3:\n- Lines 117- 118: broth microdilution and agar dilution are very different approaches?\nThe same isolate could have different AMR phenotypes using these two different\nmodels. How do you think this will impact the model?\nResponse 1.3:\nGenerally, larger training datasets correlate with improved model performance.\nAlthough EUCAST prioritises broth microdilution for antimicrobial susceptibility testing\n(AST), CLSI guidelines suggest that both agar dilution and broth microdilution are\nsuitable for AST in P. aeruginosa (except colistin, which was not assessed in our study)\n[1]. As a result, we incorporated agar dilution data to augment the training set. Notably,\nthese data covered only four antimicrobials (i.e., tobramycin, ceftazidime, ciprofloxacin,\nand meropenem), achieving strong AUROCs ranging from 0.869 to 0.977. These\nresults suggest that expanding the dataset via the inclusion of agar dilution data\nenhanced, rather than compromised, model performance.\nReferences\n1. CLSI. M100 - Performance standards for antimicrobial susceptibility testing.\nClinical and laboratory standards institute Wayne, PA; 2020.\nComment 1.4:\n- Line 123-126: I don’t really understand why you selected the external validation data\nfrom two small datasets rather than pooling the data and randomly selecting the data.\nThis could have avoided some of the phenotype imbalances.\nResponse 1.14:\nThe aforementioned steps (i.e., data pooling and random test set selection) were\nalready performed for the internal test set. In this section, our goal was to evaluate\nhow well AMR-GNN generalises to unseen data, while maximising the training data\navailable. Therefore, we selected the two smallest datasets for external validation. We\nadded this clarification to the manuscript.\nLine 131 reads:\n“We used seven datasets (our centre and [11, 17, 19-21, 23, 24]) for training and\ninternal validation. The two smallest datasets [18, 22] were reserved as a hold-out set\nto assess the model’s generalisability (see Methods).”\nComment 1.5:\n- Some of the datasets are quite imbalanced, which could cause issues with overfitting.\nResponse 1.5:\nWe thank the reviewer for highlighting the issue of class imbalance. In this study, we\ncalculated class-specific weights and applied them to the binary cross-entropy loss\nfunction to account for imbalance. We have updated this in the manuscript.\nLine 474 reads:\n“To compute the loss, we used binary cross-entropy function (BCELoss) adjusted by\nclass-specific weights.”\nComment 1.6:\n- Line 146: Would it be possible to give a little bit more detail of what a FCGR is?\nResponse 1.6:\nWe have updated the manuscript to provide a more detailed explanation of the FCGR\nencoding process\nLine 154 reads:\n“FCGR generates a 2𝑘 ×2𝑘 grid where k-mers are mapped according to their\nnucleotide sequence and weighted by their occurrence frequency [29,30]. In this\nrepresentation, each grid cell is analogous to a pixel in an image.\nComment 1.7:\n- Lines 308-309: Do you think this drop in prediction accuracy could be due to the\nmodels being overfit, or that they did not have all the data required to train a model for\nbetter predictions?\nResponse 1.7:\nWe agree with the reviewer that the model may lack sufficient data to optimise\npredictive performance. Given that AMR mechanisms can be geographically specific\n[1], key resistance determinants present in one region may be absent in another. This\nlikely accounts for why ARDaP surpassed AMR-GNN in predicting resistance to\nmeropenem, ciprofloxacin, and piperacillin/tazobactam in the hold-out test set, as\nARDaP may incorporate a broader, globally curated list of AMR mechanisms. We have\nupdated the discussion section to reflect this point.\nReferences:\n1. Kocsis B, Gulyás D, Szabó D. Diversity and Distribution of Resistance Markers\nin Pseudomonas aeruginosa International High-Risk Clones. Microorganisms.\n2021;9(2):359.\nLine 330 reads:\n“The observed performance drop in the external test set indicates that larger training\ndatasets are likely required to improve generalisation, particularly for species with\ncomplex AMR mechanisms like P. aeruginosa. Resistance mechanisms display\nmarked regional specificity, meaning that variants widespread in one location may be\nabsent elsewhere [47]. This likely explains why ARDaP, which uses a comprehensive,\nglobally curated AMR determinant database, outperformed AMR-GNN in 3 out of 6\nexternal comparisons. These results therefore highlight the necessity of continuous\ndata integration and model updating to ensure robustness across different\ngeographical regions and time periods.”\nComment 1.8:\n- I find the benchmarks a bit limited. I am not familiar with any of the benchmark\nmethods; therefore, I think the phrase “state-of-the-art rule-based” is exaggerated,\npossibly? The authors do not mention tools such as ResFinder, which is much more\nhighly cited. Do the others not mention these types of tools because they are less\ncomplex (i.e. not a neural network model?) or for a different reason?\nResponse 1.8:\nPer the reviewer’s suggestion, we have been more conservative and removed the\nterm 'state-of-the-art'. The benchmarking methods discussed were specifically\ndesigned for AMR phenotype prediction in P. aeruginosa. ARDaP, in particular, can\nbe viewed as an enhanced version of ResFinder, integrating both ResFinder’s AMR\ndeterminant database and novel P. aeruginosa-specific resistance variants identified\nthrough GWAS and machine learning analyses. This method has demonstrated\nsuperior performance compared to several established AMR phenotype prediction\ntools, including ResFinder and AMRFinderPlus. We therefore revised the manuscript\nto clarify the choice of these benchmarks.\nLine 218 reads:\n“AMR-GNN achieved superior performance compared with P. aeruginosa-specific\nAMR phenotype prediction approaches.”\nLine 222 reads:\n“We evaluated AMR-GNN against two specific implementations: the method by\nCortes-Lara et al. [20], (hereafter ’rule-based’), which utilises the presence/absence\nof known AMR determinants; and ARDaP [32], an automated scoring system\ncombining known determinants with variants identified via genome-wide association\nstudies (GWAS) and ML-based feature selection. Notably, ARDaP previously\noutperformed other common AMR phenotype prediction tools (e.g., abritAMR,\nAMRFinderPlus, and Resfinder) in all 10 tested antimicrobials.”\nComment 1.9:\n- Many of the tools do not have version numbers, which are necessary for\nreproducibility.\nResponse 1.9:\nWe have updated the version number for the tools as suggested.\nComment 1.10:\n- The GitHub for AMR-GNN lacks information about what the model uses to predict\nand what outputs are expected. I ran the demo data, and the code worked to my\nknowledge (the Conda command did not work for me and required a bit of tweaking\nbefore I could install the software). I have previously built different types of neural\nnetworks, so I am familiar with the terminology. However, the output files do not have\nenough information for someone not familiar with neural networks. I would suggest\nthat the authors provide a summary output file and explain this in the README.md\nfile. The GitHub should explain in detail what kind of input is expected and how the\nuser can prepare their data to be in the correct format. A help menu, either on GitHub\nor on the command line, would be helpful.\nResponse 1.10:\nWe thank the reviewer for the suggestions. We have implemented Docker\ncontainerisation to streamline the environment setup process and improve code\nreproducibility. We have also developed a Snakemake workflow to generate input data\nfor AMR-GNN from genome assemblies. Furthermore, we have updated the output\nformat to include predicted binary AMR phenotypes, making the results file easier to\ninterpret. Detailed instructions have been added to the GitHub repository. Please see\nhttps://github.com/andyvng/amr-gnn.\nComment 1.11:\n- The paper also explained how the models are biologically interpretable. Could this\nbe explained in the GitHub on how I can interpret the results this way, or is this not\ncurrently added into this version?\nResponse 1.11:\nWe have updated the GitHub repository to include instructions on running model\ninterpretation, as suggested. Please refer to the Model interpretation section.\nComment 1.12:\n- Line 441: The authors mention a batch size of 32 for the building of their model. This\nmay be a bit small when training with >2000 genomes. I would like the authors to\nexplain why this is an appropriate method, in case I have misunderstood something.\nResponse 1.12:\nWhile the dataset comprised of 2,515 genomes in total, label sparsity remains an issue\nfor some antimicrobials (with as few as 601 entries for cefepime). Therefore we used\nmini-batch gradient descent to maintain training stability. We selected a batch size of\n32, which is established in the literature as an effective default. We have updated the\nmanuscript with references supporting this hyperparameter choice.\nLine 475 reads:\n“We employed mini-batch gradient descent with a batch size of 32, which is regarded\nas a robust empirical baseline [60, 61].”\nComment 1.13:\n- Line 51: “detection of AMR” could be made clearer by saying “detection of AMR\nphenotype”\nResponse 1.13:\nWe have updated the manuscript accordingly.\nLine 51 reads:\n“Rapid and accurate detection of AMR phenotype through antimicrobial susceptibility\ntesting (AST) is crucial for improving patient outcomes”\nComment 1.14:\n- Line 54: “Our current methods for identifying” Why not say “Culture-based methods…”\nto avoid confusion of when people use WGS techniques?\nResponse 1.14:\nWe have updated the manuscript as suggested.\nLine 54 reads:\n“Culture-based methods for identifying AMR are slow, laborious and are often not\nreported to the clinician for 2-5 days after sample collection”\nComment 1.15:\n- Line 102: “multiple genome representations” possibly is not the best way to phrase\nit?\nResponse 1.15:\nWe have replaced “multiple genome representations” with “multiple WGS-derived\nfeatures”.\nLine 102 reads:\n“We therefore developed AMR-GNN, a new GNN-based framework using multiple\nWGS-derived features to predict AMR phenotype in P. aeruginosa.”\nComment 1.16:\n- Line 121: I assume that there are not 12 MIC values for each isolate; it would be nice\nto say how many MIC values there are in total\nResponse 1.16:\nWe have added the total number of MIC values as suggested.\nLine 126 reads:\n“The AST dataset consisted of 19,865 MIC entries covering 12 common\nantipseudomonal antimicrobials.”\nComment 1.17:\n- Line 122: “non-sensitive” is used to describe resistant isolates; why not just say\n“resistant” to avoid confusion about “intermediate” phenotypes being included with\nnon-sensitive?\nResponse 1.17:\nWe have replaced “non-sensitive” with “resistant” as suggested.\nComment 1.18:\n- Line 148: Why not mention the feature selection here?\nResponse 1.18:\nIn this initial analysis, the entire feature sets were provided to the elastic net models,\nallowing the algorithm to identify the most predictive features. We have updated the\nmanuscript accordingly.\nLine 158 reads:\n“The complete feature sets for unitigs and SNPs contained approximately 1.5 million\nand 900,000 features, respectively, which were used as input into the elastic net.”\nComment 1.19:\n- Line 152: Define the acronym “AUROC”\nResponse 1.19:\nWe have defined AUROC as suggested.\nLine 164 reads:\n“area under receiver operating characteristic curve (AUROC)”\nComment 1.20:\n- Lines 180-183: The sentence starting with “Bacteria” is quite a long sentence.\nResponse 1.20:\nWe have rewritten the sentence as suggested.\nLine 192 reads:\n“Bacterial population structure is a major confounder in AMR prediction because of\nslow linkage disequilibrium decay [31]. This results in co-inheritance of causal and\nnon-causal variants that ML models cannot easily disentangle.”\nComment 1.21:\n- Line 225: The numbers relating to model performance are confusing.\nResponses 1.21:\nWe have rewritten the sentences accordingly.\nLine 235 reads:\n“Based on label availability, we benchmarked AMR-GNN against a rule-based\napproach, ARDaP, and VAMPr using both internal and external test sets (Fig. 3). On\nthe internal test set, AMR-GNN achieved universally higher F1-scores and better very\nmajor error rates (VME) across all tested antimicrobials (n=5, 8, and 8, respectively).\nIn the external validation (comprising 5, 6, and 6 antimicrobials), AMR-GNN\ndemonstrated superior F1-scores in 4/5, 3/6, and 4/6 cases, respectively. For VME,\nAMR-GNN achieved better results for 5/5, 4/6, and 4/6 cases, respectively.”\nComment 1.22:\n- Line 239: ‘PATRIC’ is now called BV-BRC\nResponse 1.22:\nWe thank the reviewer for pointing this out. We have replaced PATRIC with BV-BRC\naccordingly.\nLine 252 reads:\n“The analysis covered a comprehensive panel of antimicrobials specific to each\nspecies, sourced from The Bacterial and Viral Bioinformatics Resource Center (BV-\nBRC) databases [37, 38]”\nComment 1.23:\n- Lines: 368-369: The authors talk about applying their models to “multi-omics data”,\nwould this be possible if they are taxonomically specific? Or will the models eventually\nbe more flexible this way?\nResponse 1.23:\nAlthough the current implementation uses WGS-derived representations to construct\nnode and edge features, the framework is inherently flexible and allows for the\nsubstitution of these inputs with other ‘omics data. We have cited studies\ndemonstrating the efficacy of non-genomic data in AMR phenotype prediction,\nsuggesting that their integration could further enhance predictive power. However, the\nscarcity of datasets encompassing all such modalities simultaneously remains a\nlimiting factor, presenting a significant opportunity for future exploration.\nLine 399 reads:\n“AMR-GNN presents an interpretable and flexible framework for such integration, with\npromising potential for application to multi-omics modalities such as proteomics and\ntranscriptomics, which have also demonstrated utility in predicting AMR phenotype [11,\n51, 52].”\nREVIEWER #1 (Remarks on code availability)\nComment 1.24:\nThe GitHub for AMR-GNN lacks information about what the model uses to predict and\nwhat outputs are expected. I ran the demo data, and the code worked to my knowledge\n(the Conda command did not work for me and required a bit of tweaking before I could\ninstall the software). I have previously built different types of neural networks, so I am\nfamiliar with the terminology. However, the output files do not have enough information\nfor someone not familiar with neural networks. I would suggest that the authors provide\na summary output file and explain this in the README.md file. The GitHub should\nexplain in detail what kind of input is expected and how the user can prepare their data\nto be in the correct format. A help menu, either on GitHub or on the command line,\nwould be helpful.\nResponse 1.24:\nThis comment overlaps with comment 1.10. Please refer to response 1.10.\nComment 1.25:\nThe paper also explained how the models are biologically interpretable. Could this\nbe explained in the GitHub on how I can interpret the results this way, or is this not\ncurrently added into this version?\nResponse 1.25:\nThis comment overlaps with comment 1.11. Please refer to response 1.11.\nComment 1.26:\nLine 441: The authors mention a batch size of 32 for the building of their model. This\nmay be a bit small when training with >2000 genomes. I would like the authors to\nexplain why this is an appropriate method, in case I have misunderstood something.\nResponse 1.26:\nThis comment overlaps with comment 1.12. Please refer to response 1.12.\nREVIEWER #2 (Remarks to the Author)\nComment 2.1:\n_ benchmarks on other bacterial pathogens: it is impossible to assess the robustness\nof AMR-GNN performance without reference points (i.e., results obtained from other\ntools). I would kindly ask the authors to run VAMPr or ARIBA, or any AMR phenotype\nprediction tool, on these four datasets.\nResponse 2.1:\nWe thank the reviewer for the suggestion. We have now expanded our analysis to\ninclude a comparison between AMR-GNN and ResFinder across all other bacterial\npathogens. ResFinder was selected as the benchmark due to its extensive\nantimicrobial coverage and superior performance on divergent genomic datasets,\nwhere it frequently surpasses other approaches, including ML-based tools [1]. This\nmakes it particularly suitable for benchmarking against the globally sourced BV-BRC\ndataset. Overall, AMR-GNN outperformed ResFinder in the majority of species–\nantimicrobial combinations. We have updated the manuscript accordingly.\nReferences:\n1. Hu K, Meyer F, Deng Z-L, Asgari E, Kuo T-H, Münch PC, et al. Assessing\ncomputational predictions of antimicrobial resistance phenotypes from\nmicrobial genomes. Briefings in Bioinformatics. 2024;25(3). doi:\n10.1093/bib/bbae206.\nLine 264 reads:\n“In terms of benchmarking, we compared the performance of AMR-GNN with\nResFinder, a tool that has shown strong results relative to several other AMR\nphenotype prediction methods [5]. Specifically, we assessed predictions across 16\nspecies-antimicrobial combinations. In terms of F1-score, AMR-GNN outperformed\nResFinder in 14 of these 16 comparisons. The only exceptions were tobramycin in E.\ncoli (0.834 vs. 0.859) and fusidic acid in S. aureus (0.929 vs. 0.969) (Fig. 4i). The most\nsignificant performance improvement with AMR-GNN was observed for linezolid in E.\nfaecium (0.859 vs 0.512).”\nComment 2.2:\n_ content of the discussion section: while I found the overall manuscript very\ninteresting, I’m afraid the Discussion section was particularly poor. The first five\nparagraphs consist of a long, and perhaps unnecessary, recapitulation of the results.\nThe two paragraphs that follow mention limitations of the study (see below), but there\nis no real discussion of the analyses. I would have liked some discussion of, for\ninstance, the contribution of each feature type to performance, given their different\nnature, and which additional ones could be added in the future. Also, how do the\nauthors know that ST is the optimal distance level to remove population effects?\nResponse 2.2:\nWe thank the reviewer for the suggestions to improve the Discussion part. Accordingly,\nwe have condensed the section to improve clarity and flow. Please refer to the revised\nDiscussion section in the manuscript.\nRegarding the reviewer’s specific suggestions, we first discussed how feature\nselection might affect model performance. As multi-omics data are increasingly\ncollected, we provide recommendations on how these data can be integrated into the\nmodels and what features may warrant inclusion.\nLine 318 reads:\n“Consistent with previous work [7], we found that reference-free genomic features (i.e.,\nunitigs) achieved the best performance among single-feature models. However, we\nalso observed that incorporating pairwise genomic distances, which are widely used\nfor detecting outbreaks [46], as edge features significantly improved predictive power.\nThis multi-view integration likely accounts for the model's superior performance over\nsingle-feature models and P. aeruginosa-specific rule-based approaches across all\ntested antipseudomonal agents. These findings encourage the integration of\nadditional data types to further enhance performance. For instance, although it is\ncostly to obtain, incorporating transcriptomic data as node features warrants\nconsideration for P. aeruginosa given its capacity for environment-driven\ntranscriptional adaptation [47].”\nFinally, we acknowledge the limitation of the use of MLST to account for population\nstructure.\nLine 383 reads:\n“Third, while our approach yielded performance improvements, we acknowledge the\ninherent limitations of using ST to account for population structure bias. While ST data\nis widely available and generally informative, its dependence on a limited set of\nhousekeeping genes disregards significant genomic variation., Future iterations could\ntherefore benefit from employing higher-resolution typing schemes, such as core-\ngenome MLST (cgMLST), to better address lineage biases.”\nComment 2.3:\n_ applicability of the algorithm: It isn’t clear to me after reading the manuscript whether\nAMR-GNN represents a preliminary attempt at integrating different sources of features\nor a ready-to-use tool that could be deployed for large-scale AMR inferences. The\nauthors mention two limitations regarding its applicability: the need to rebuild the\n“database” (I’m afraid I did not understand this part) and its “computational complexity”\n(are the authors referring to runtime and memory usage?). However, I believe these\naspects should be mentioned in the abstract and introduction to let the reader fully\nunderstand the current applicability of AMR-GNN.\nResponse 2.3:\nWe thank the reviewer for the suggestion. We have revised the abstract and\nintroduction to explicitly frame this study as a proof-of-concept. Our objective is to\ndemonstrate the potential of a graph neural network using multi-representation inputs\nto enhance AMR phenotype prediction relative to single-feature approaches. Although\nthe current iteration relies exclusively on WGS-derived features, the framework is\ndesigned for multimodal learning and is capable of integrating other ‘omics layers (e.g.,\nproteomic or transcriptomic) when those data become available.\nLine 40 reads:\n“We present AMR-GNN as a proof-of-concept framework designed to address several\nkey problems in AMR phenotype prediction with machine learning (ML) approaches,\nincluding using multiple genomic representations to enhance performance, to mitigate\nthe influence of clonal relationships and to identify informative biomarkers to provide\nexplainability.”\nLine 113 reads:\n“Collectively, these results demonstrate the potential of leveraging distinct feature\ntypes to enhance AMR phenotype prediction, establishing AMR-GNN as a robust\nproof-of-concept framework for integrating these features into a unified ML\narchitecture.”\nWe have revised the manuscript to clarify the need to reconstruct the unitig database.\nLine 376 reads:\n“While new isolates can be queried against an existing unitig list, this approach may\nobscure novel biological signals. For instance, if two isolates have distinct mutations\nat the same nucleotide position, the reference unitig would be recorded as “absent”\nfor both. Without updating the database to include the specific unitigs for these new\nvariants, the genetic nuance distinguishing these isolates is lost.”\nFinally, the term “computational complexity'” refers to the preprocessing step required\nto generate feature inputs for AMR-GNN, specifically the computation of pairwise\ndistance matrices, where the cost increases quadratically with each added isolate.\nHowever, this constraint was primarily relevant to the benchmarking against simpler\nrule-based approaches for other pathogens (i.e., E. coli, K. pneumoniae, S. aureus, E.\nfaecium). As we addressed this in Response 2.1, we have removed this specific\ndiscussion from the manuscript to avoid confusion.\nComment 2.4:\n_ the GitHub repo could benefit from more detailed instructions regarding AMR-GNN\nusage. There is currently no manual.\nResponse 2.4:\nWe have updated the GitHub repository to include detailed instructions for using AMR-\nGNN. This documentation covers the pre-processing pipeline for genome assemblies\nas well as the Docker container for executing the code. Please see\nhttps://github.com/andyvng/amr-gnn\nComment 2.5:\n_ supplementary file 1 is a PDF consisting of two tables spanning 270 pages. Could\nthe authors provide these data as an Excel file, which would be easier to read and\nextract data from?\nResponse 2. 5:\nSorry for the inconvenience. Supplementary File 1 was originally uploaded as an Excel\nfile but was automatically converted to PDF by the submission system. We have\nprovided a download link for the original Excel file below.\nhttps://docs.google.com/spreadsheets/d/1_wXfO-HSKPXO3oajZLqvW79DOtbRompS\nComment 2.6:\n_ Introduction, page 4: the “high-dimensional nature” of genomic data is not sufficiently\nexplained in the introduction. I personally thought the authors were referring to higher-\norder features (e.g., impact of variants on promoter activity or protein stability).\nResponse 2.6:\nIn this context, 'high-dimensional' refers to the sequence of millions of nucleotides\ncomprising bacterial WGS data (e.g., approximately 6 Mb for P. aeruginosa). We have\nrevised this section of the manuscript to ensure this interpretation is clear.\nLine 79 reads:\n“Given that bacterial genomes consist of sequences containing millions of nucleotides\n(e.g., approximately 6 Mb for P. aeruginosa), WGS data can be represented in multiple\nways for input into ML models.”\nComment 2.7:\n_ l. 100–101: “their use in bacterial genomics for AMR prediction remains limited.” If\nGNNs have already been used for AMR prediction, references are needed. If not, the\nsentence might need slight rewording.\nResponse 2.7:\nWe have added the reference as suggested.\nLine 101 reads:\n“their use in bacterial genomics for AMR phenotype prediction remains limited [15].”\nComment 2.8:\n_ l. 286–287: the model does not make any predictions per se of horizontally\ntransferred AMR genes. Please reword.\nResponse 2.8:\nWe have updated the manuscript accordingly\nLine 306 reads:\n“This enables our model to not only generate informative AMR embeddings but also\ncapture shared genetic content among bacterial isolates.”\nComment 2.9:\n_ l. 377: please define the abbreviation “BMD.”\nResponse 2.9\nWe have added definition for BMD\nLine 410 reads:\n“broth microdilution (BMD)”\nComment 2.10:\n_ l. 377: please also indicate here how many of these isolates were collected in this\nstudy (I believe the number is 341).\nResponse 2.10:\nWe have added the detail as suggested\nLine 413 reads:\n“For our in-house collection (n=341), BMD was performed using the SensititreTM\nGram negative plate (ThermoFisher, plate code: DKMNG).”\nComment 2.11:\n_ l. 390: eight, not seven.\nResponse 2.11:\nWe have corrected the typo accordingly.\nLine 423 reads:\n“Consequently, we conducted external validation for eight antimicrobials”\nComment 2.12:\n_ l. 400: please clarify what “internal” means. If it corresponds to the unpublished set\nof isolates, then “remaining data” in l. 392 should be modified into “remaining published\ndata.”\nResponse 2.12:\nWe have revised the manuscript to clarify the distinction between the internal and hold-\nout test sets, with changes marked in red.\nLine 418 reads:\n“First, for external validation, we identified two collections with the smallest sample\nsizes (42 and 21 isolates) and set them aside as a hold-out set (referred to as the\n“external test set”) [18, 22]. The hold-out dataset contained AST results for nine\nantimicrobials; however, we excluded imipenem from the external validation due to\nabsolute resistance rate (100%). Consequently, we conducted external validation for\neight antimicrobials: meropenem, tobramycin, amikacin, ciprofloxacin,\nceftolozane/tazobactam, ceftazidime, piperacillin/tazobactam, and aztreonam. We\nsplit the remaining data into training and testing sets using an 80/20 stratified split\nbased on AST labels for each antimicrobial. This test set is referred to as the “internal\ntest set”. To account for performance variability, this process was repeated 10 times\nwith random seeds. For consistency, the same training/testing splits were used across\nall experiments, including models based on single genomic representations, AMR-\nGNN training, and benchmarking with external AMR phenotype prediction tools. For\nAMR-GNN and the CNN model using FCGR inputs, the training set was further divided\ninto training (80%) and validation (20%) subsets. These models were trained on the\nreduced training set to minimise validation loss. The final models were evaluated on\nboth internal and external test sets.”\nComment 2.13:\n_ l. 439: consisting “of”?\nResponse 2.13:\nWe have corrected the typo accordingly.\nLine 473 reads:\n“consisting of 3 convolutional layers and a final classification head.”\nREVIEWER #3 (Remarks to the Author)\nComment 3.1:\nThis manuscript proposes AMR-GNN, a graph deep learning–based framework that\nintegrates multiple genomic representations with graph neural networks (GNN) to\npredict antimicrobial resistance (AMR) from genomic sequence data of Pseudomonas\naeruginosa. Although Figure 1 illustrates that the features are mainly derived from\nunitigs, SNPs, and FCGR, the description in the Methods section and the demo\ndataset provided at https://github.com/andyvng/amr-gnn suggest that AMR prediction\ndoes not directly start from genomic assemblies, but rather requires an adjacency\nmatrix as input. However, the information on the website appears insufficient to clarify\nhow genomic assemblies can be practically input to obtain AMR predictions. Additional\nmethodological transparency would therefore be beneficial.\nResponse 3.1:\nWe have implemented a pre-processing workflow to transform genome assemblies\ninto model-compatible feature inputs. Please see https://github.com/andyvng/amr-gnn\nComment 3.2:\nFurthermore, while the study employs GNN to evaluate performance using metrics\nsuch as F1, AUROC, sensitivity, and specificity, it overlooks what is arguably the most\nclinically critical metric in AMR prediction: the very major error (VME) rate.\nResponse 3.2:\nWe thank the reviewer for pointing out VME metrics. We have now added results for\nboth VME and major error rates (ME). Please see Supp. Table 3 and Supp. Table 8.\nWe have also revised the manuscript accordingly.\nLine 486 reads:\nIn addition, we also reported clinically related metrics, i.e., VME and ME:\n𝑅𝑒𝑠𝑖𝑠𝑡𝑎𝑛𝑡 𝑖𝑠𝑜𝑙𝑎𝑡𝑒𝑠 𝑓𝑎𝑙𝑠𝑒𝑙𝑦 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 𝑎𝑠 𝑠𝑢𝑠𝑐𝑒𝑝𝑡𝑖𝑏𝑙𝑒\n𝑉𝑒𝑟𝑦 𝑚𝑎𝑗𝑜𝑟 𝑒𝑟𝑟𝑜𝑟 (𝑉𝑀𝐸) =\n𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑟𝑒𝑠𝑖𝑠𝑡𝑎𝑛𝑡 𝑖𝑠𝑜𝑙𝑎𝑡𝑒𝑠\n𝑆𝑢𝑠𝑐𝑒𝑝𝑡𝑖𝑏𝑙𝑒 𝑖𝑠𝑜𝑙𝑎𝑡𝑒𝑠 𝑓𝑎𝑙𝑠𝑒𝑙𝑦 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 𝑎𝑠 𝑟𝑒𝑠𝑖𝑠𝑡𝑎𝑛𝑡\n𝑀𝑎𝑗𝑜𝑟 𝑒𝑟𝑟𝑜𝑟 (𝑉𝑀𝐸) =\n𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑠𝑢𝑠𝑐𝑒𝑝𝑡𝑖𝑏𝑙𝑒 𝑖𝑠𝑜𝑙𝑎𝑡𝑒𝑠\nThe optimal threshold was determined by minimising the weighted sum of VME and\nME:\n𝑂𝑝𝑡𝑖𝑚𝑎𝑙 𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑 = 𝑎𝑟𝑔𝑚𝑖𝑛(𝜆𝑉𝑀𝐸 +𝑀𝐸)\nGiven that the Food and Drug Administration (FDA) establishes stricter acceptable\nthresholds for VME (1.5%) compared to ME (3%) for new AST systems [60], we\napplied a weighting factor of 𝜆=2 to prioritise VME minimisation.\nLine 236 reads:\n“On the internal test set, AMR-GNN achieved universally higher F1-scores and better\nvery major error rates (VME) across all tested antimicrobials (n=5, 8, and 8, respectively).\nIn the external validation (comprising 5, 6, and 6 antimicrobials), AMR-GNN\ndemonstrated superior F1-scores in 4/5, 3/6, and 4/6 cases, respectively. For\nVME, AMR-GNN achieved better results for 5/5, 4/6, and 4/6 cases, respectively.”\nLine 263 reads:\n“VME values ranged from 0.008 to 0.133, while ME values ranged from 0.054 to 0.336.”\nLine 350 reads:\n“Although VME and ME are critical for clinical translation, they are rarely detailed in\nAMR phenotype prediction literature. Compared to reported values for K. pneumoniae\nand S. aureus [48], our model achieved better VME rates in most cases (5/6 and 3/3,\nrespectively). The mixed performance regarding ME (superior in 2/6 and 1/3\ncomparisons) is attributable to our threshold optimisation process, which assigned a\nhigher penalty weight to VME.”\nComment 3.3:\nIn addition, the reported F1 scores (Figure 3) are approximately 0.8, which is lower\nthan those achieved by prior studies (e.g.,\nhttps://pmc.ncbi.nlm.nih.gov/articles/PMC8575023/), where F1 values around 0.92\nwere reported (link to model stats). As a result, it is not entirely clear whether this\nGNN-based approach offers superior performance compared to existing methods.\nResponse 3.3:\nWe thank the reviewer for highlighting the average F1 scores from previous studies.\nHowever, a precise head-to-head comparison is difficult, as the cited study presents\nthese values only as a color-coded heatmap (Figure 5) without providing raw\nnumerical data. Based on the colour scale, the high mean F1 scores reported appear\nto be driven by species not included in our dataset, such as Campylobacter jejuni,\nClostridium difficile, and Neisseria gonorrhoeae. Conversely, the performance for P.\naeruginosa appears to fall within the average range. To address the broader context,\nwe have included F1 scores for additional species from the BV-BRC database in Supp.\nTable 8. These results demonstrate that AMR-GNN achieves strong performance\nacross these additional species, reaching F1 scores as high as 0.979 for K.\npneumoniae-ceftriaxone (please see Supp. Table 8).\nREVIEWER #3 (Remarks on code availability):\nComment 3.4:\nAlthough I have not reviewed the code at https://github.com/andyvng/amr-gnn, the\ndocumentation provided on the website does not include instructions on how to train\nthe AMR-GNN model or how to use genomic sequencing data to perform AMR\nprediction. Clearer guidance on these steps would greatly enhance the reproducibility\nand utility of the proposed framework.\nResponses 3.4:\nWe have updated the Github repository to include instructions to train AMR-GNN and\nperform prediction. Please see https://github.com/andyvng/amr-gnn.\nDear Reviewers,\nThank you for your careful consideration of our manuscript ‘AMR-GNN: A multi-\nrepresentation graph neural network framework to enable genomic antimicrobial\nresistance prediction’. Your comments are much appreciated, and we believe your\ninput has strengthened the manuscript. Please find our point-by-point response to your\ncomments below.\nREVIEWER #1:\nComment 1.1:\nI think it would be nice to have a table of each parameter/option used in the command\nand have a mini explanation of what that option does (even if it seems obvious).\nResponse 1.1:\nWe have added tables with argument and brief description to instruct how to train the\nmodel to the Github repository. Please see https://github.com/andyvng/amr-gnn.\nComment 1.2:\nThe authors have implemented a Snakemake workflow using a Docker image.\nHowever, on some systems, Docker is not feasible (I currently experience this on the\nHPC I use for work), so you could include instructions on how to build a Singularity\ncontainer, too. I do not necessarily think this is needed for publication, but it may be\nnice in the future for people who are less experienced using containers.\nResponse 1.2:\nWe thank the reviewer for the helpful suggestion. Because Apptainer (formerly\nSingularity) images can be built from Docker images, we have now included\ninstructions on how to build the Apptainer image and how to run the training and\nprediction workflows. Please see https://github.com/andyvng/amr-gnn.\nComment 1.3:\nI tested the new code, and each stage worked apart from the model interpretation\nsection, this should be fixed before publication. I think it is this line in “explain.py” that\nis causing the pipeline to fail: baseline = torch.load(cfg.explainer.baseline).to(device).\nResponse 1.3:\nWe thank the reviewer for pointing out this issue. The code has now been corrected\naccordingly.\nREVIEWER #3\nComment 3.1:\nAs Reviewer #3, my comments primarily focused on the practical usability of the code\nprovided on GitHub. Although the authors state that detailed instructions are available\nonline, I encountered substantial difficulties during installation and was unable to fully\ntest the workflow. In particular, I was not able to use the Docker environment, which\nprevented me from verifying whether the provided pipeline is functional as described.\nMoreover, the current documentation on the GitHub repository\n(https://github.com/andyvng/amr-gnn\n) remains overly simplified and does not sufficiently guide users through installation,\ninput preparation, and execution in a way that ensures reproducibility. Based on my\nexperience, the existing instructions are insufficient to guarantee that an independent\nuser can reliably reproduce or deploy the tool, especially in environments where\nDocker cannot be used.\nWhile the authors indicate that usage instructions are provided on GitHub, I\nexperienced difficulties during installation and was unable to fully evaluate the\nworkflow. As I could not use the Docker environment, I was therefore unable to assess\nwhether the provided code is readily usable in practice.\nResponse 3.1:\nWe thank the reviewer for evaluating our codebase. We have now added instructions\nfor running the analysis locally using the `uv` package. Because we provide a locked\nenvironment file, `uv` can automatically create the Python environment on first use\nand reuse it on the local machine. We hope this resolves the installation issues and\nenables the analysis to run smoothly. Please see the ‘Run with uv’ sections in our\nGitHub repository.\nAdditionally, as requested by Reviewer #1, we have included a configuration table for\neach command. We hope this further clarifies the instructions and supports\nreproducibility of the workflow."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I can see that the authors have clearly listened to myself and the other reviewers to make relevant changes to the\nmanuscript.\nThe only comments I still have are about the GitHub (see comments on code).\n(Remarks on code availability)\ncomments on code:\nI think it would be nice to have a table of each parameter/option used in the command and have a mini explanation of what\nthat option does (even if it seems obvious).\nThe authors have implemented a Snakemake workflow using a Docker image. However, on some systems, Docker is not\nfeasible (I currently experience this on the HPC I use for work), so you could include instructions on how to build a\nSingularity container, too. I do not necessarily think this is needed for publication, but it may be nice in the future for people\nwho are less experienced using containers.\nI tested the new code, and each stage worked apart from the model interpretation section, this should be fixed before\npublication. I think it is this line in “explain.py” that is causing the pipeline to fail: baseline =\ntorch.load(cfg.explainer.baseline).to(device).", "reviewer_2": "The authors have carefully considered my suggestions and revised the manuscript accordingly. The changes have\nimproved the clarity and accuracy of the text. I now recommend the manuscript for publication.", "reviewer_3": "As Reviewer #3, my comments primarily focused on the practical usability of the code provided on GitHub. Although the\nauthors state that detailed instructions are available online, I encountered substantial difficulties during installation and was\nunable to fully test the workflow. In particular, I was not able to use the Docker environment, which prevented me from\nverifying whether the provided pipeline is functional as described.\nMoreover, the current documentation on the GitHub repository (https://github.com/andyvng/amr-gnn\n) remains overly simplified and does not sufficiently guide users through installation, input preparation, and execution in a\nway that ensures reproducibility. Based on my experience, the existing instructions are insufficient to guarantee that an\nindependent user can reliably reproduce or deploy the tool, especially in environments where Docker cannot be used.\n(Remarks on code availability)\nWhile the authors indicate that usage instructions are provided on GitHub, I experienced difficulties during installation and\nwas unable to fully evaluate the workflow. As I could not use the Docker environment, I was therefore unable to assess\nwhether the provided code is readily usable in practice."}, "author_response": ""}]

Decision letter