Title: Supervised Graph Contrastive Learning for Gene Regulatory Network
Venue: NeurIPS 2025
Accepted: no
Keywords: Graph Contrastive Learning

Gene Regulatory Network

Life Science
Abstract: Graph representation learning is effective for obtaining a meaningful latent space utilizing the structure of graph data and is widely applied, including biological networks.
In particular, Graph Contrastive Learning (GCL) has emerged as a powerful self-supervised method that relies on applying perturbations to graphs for data augmentation. 
However, when applying existing GCL methods to biological networks such as Gene Regulatory Networks (GRNs), they overlooked meaningful biologically relevant perturbations, e.g., gene knockdowns. 
In this study, we introduce SupGCL (Supervised Graph Contrastive Learning), a novel GCL method for GRNs that directly incorporates biological perturbations derived from gene knockdown experiments as the supervision. 
SupGCL mathematically extends existing GCL methods that utilize non-biological perturbations to probabilistic models that introduce actual biological gene perturbation utilizing gene knockdown data. 
Using the GRN representation obtained by our proposed method, our aim is to improve the performance of biological downstream tasks such as patient hazard prediction and disease subtype classification (graph-level task), and gene function classification (node-level task).
We applied SupGCL on real GRN datasets derived from patients with multiple types of cancer, and in all experiments SupGCL achieves better performance than state-of-the-art baselines.

Abstract

Graph representation learning is effective for obtaining a meaningful latent space utilizing the structure of graph data and is widely applied, including biological networks. In particular, Graph Contrastive Learning (GCL) has emerged as a powerful self-supervised method that relies on applying perturbations to graphs for data augmentation. However, when applying existing GCL methods to biological networks such as Gene Regulatory Networks (GRNs), they overlooked meaningful biologically relevant perturbations, e.g., gene knockdowns. In this study, we introduce SupGCL (Supervised Graph Contrastive Learning), a novel GCL method for GRNs that directly incorporates biological perturbations derived from gene knockdown experiments as the supervision. SupGCL mathematically extends existing GCL methods that utilize non-biological perturbations to probabilistic models that introduce actual biological gene perturbation utilizing gene knockdown data. Using the GRN representation obtained by our proposed method, our aim is to improve the performance of biological downstream tasks such as patient hazard prediction and disease subtype classification (graph-level task), and gene function classification (node-level task). We applied SupGCL on real GRN datasets derived from patients with multiple types of cancer, and in all experiments SupGCL achieves better performance than state-of-the-art baselines.

1 Introduction

Graph representation learning has recently attracted attention in various fields to learn a meaningful latent space to represent the connectivity and attributes in given graphs [1]. Applications of graph representation learning are advancing in numerous areas where network data exists, such as analysis in social networks, knowledge graphs [2, 3], and biological network analysis in bioinformatics [4, 5].

Among these applications, the use of graph representation learning for Gene Regulatory Networks (GRNs), where each node and edge represents important intracellular functions and/or processes, is particularly significant in the fields of biology and drug discovery, as it is expected to contribute to identifying therapeutic targets and understanding disease mechanisms. GRN representation learning has been applied to tasks such as inferring transcription factors [6] and predicting drug responses in cancer cell lines [7].

With advancements in gene expression measurement and analysis technologies, identification methods for GRNs from expression data are also evolving. Traditional GRN identification constructs networks using statistical techniques applied to patient populations. Recently, it has become possible to construct GRNs specific to individual patients, highlighting distinct gene regulatory patterns compared to the population as a whole [8]. Hereafter in this paper, we refer to such individualized networks simply as GRNs. Similarly, in cell-based experiments such as gene knockdowns, it is now possible to estimate distinct GRNs for each experiment depending on the expression profile.

Graph representation learning applied to GRNs is believed to be effective for a wide range of 37 biological applications. Among such methods, Graph Contrastive Learning (GCL) has gained 38 traction. GCL enhances graph data via artificial perturbations applied to nodes or edges, and learns 39 useful graph embeddings by maximizing the similarity between differently augmented views of the 40 same graph [9]. For example, we can perform contrastive learning by artificial perturbations such as 41 randomly removing nodes to augment the GRN and consequently learn a representation for the GRN. 42

However, a major challenge arises in that the artificial perturbations employed in conventional 43 GCL methods significantly deviate from genuine biological perturbations, making it difficult to 44 learn effective representations when applied to GRNs. A related issue occurs in heterogeneous 45 node networks, where random perturbations to nodes or edges during augmentation can disrupt 46 topology and attribute integrity, ultimately hindering representation learning [10, 11]. This problem 47 is especially relevant to GRNs, where node heterogeneity exists-for example, in the presence of 48 master regulators [12]. 49

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To address these issues, we propose a novel supervised GCL method (SupGCL) that leverages gene knockdown perturbations within GRNs. Our method uses experimental data from actual gene knockdowns as supervision, enabling biologically faithful representation learning. In gene knockdown experiments, the expression of specific genes is suppressed, representing biological perturbations that allow for the inference of GRNs. By using these perturbations as supervision signals for GCL, we can perform data augmentation that retains biological characteristics. Moreover, since our method naturally extends traditional GCL models in the direction of supervised augmentation within a probabilistic framework, conventional GCL approaches emerge as special cases of our proposed model.

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To evaluate the effectiveness of the proposed SupGCL method, we apply it to GRN datasets from cancer patients across three cancer types and conduct multiple downstream tasks. For gene-level downstream tasks, we perform classification into Biological Process, Cellular Component, and cancerrelated gene categories. For patient-level tasks, we conduct hazard prediction and disease subtype classification. The performance of our method is compared against existing graph representation learning techniques, including conventional GCL methods.

The main contributions of this study are as follows:

Proposal of a novel GRN representation learning method utilizing gene knockdown experiments: We develop a new GCL method tailored for GRNs that incorporates gene knockdown data as supervision to enhance biological plausibility.

Theoretical extension of GCL: We formulate supervised GCL, incorporating augmentation selection into a unified probabilistic modeling framework, and theoretically demonstrate that existing GCL methods are special cases of our proposed approach.

Empirical validation of the proposed method: We apply the method to 13 downstream tasks on GRNs derived from real cancer patients and consistently outperform conventional approaches across all tasks.

Our implementation and all experimental codes are available on http://github.com/xxxxxx .

2 Related Work

Graph Contrastive Learning (GCL) has inspired the development of numerous methods, largely based 77 on the design of data augmentations and the construction of positive/negative sample pairs [10]. GCL 78 methods can be broadly categorized into three types according to how they generate these training 79 pairs: (1) graph-level pairs, (2) node-level pairs, and (3) cross-model pairs. 80

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Arepresentative method using graph-level pairs is GraphCL [13], which applies random data augmen-

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tations to graphs and treats the resulting two graph views as a positive pair. This approach enhances

In contrast, methods such as GRACE, which generate node-level pairs, apply augmentations to graphs 87 and treat embeddings of different nodes as negative pairs during training [15]. This enables more 88 precise representation learning that captures local structure and attribute information at the node 89 level. Although GRACE has been applied to real GRNs [6], the augmentation strategies used do not 90 incorporate the specific biological characteristics of GRNs. 91

Beyond direct graph manipulations, methods like BGRL [16] generate positive pairs across mod92 els-between two instances of the same graph embedding model operating at different learning 93 speeds-rather than relying on heuristic graph augmentations. Such cross-model pairing strategies 94 have attracted attention for their ability to learn graph representations in a more natural manner. 95 Notably, the recently proposed SGRL [17] achieves stable and high-performance self-supervised 96 learning by using a pair of models: one that distributes node embeddings uniformly on a hypersphere, 97 and another that incorporates graph topology information. However, these methods are constrained 98 by their inability to design task-specific perturbations, and their applicability to biological networks 99 such as GRNs remains unexplored and unverified. 100

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3 Preliminaries

3.1 Background of Graph Contrastive Learning

Although there are various definitions of contrastive learning, it can be expressed using a probabilistic model based on KL divergence over pairs of augmentations or node instances [18]. Let X denote a set of entities and let ( x, y ) ∈ X × X be a pair from that set. The contrastive loss is formulated as follows:

Here, q φ ( y | x ) is the probability distribution of the target model with parameter φ , and p θ ( y | x ) is a reference distribution. To avoid trivial solutions when training both p θ and q φ simultaneously, the reference distribution p θ is almost fixed. The reference model p θ ( y | x ) is often designed as a probability that assigns a non-zero constant to positive pairs ( x, y ) and zero to negative pairs ( x, y ) .

Graph Contrastive Learning (GCL) handles the target model q φ ( j | i ) corresponding to a pair of nodes ( i, j ) . Consider graph operations for augmentation, order them, and represent the index of these operations by a . Let z a i ∈ R d be the graph embedding of the i -th node obtained from the Graph Neural Network under the a -th augmentation operation. For two augmentation operations ( a, b ) , the pair of probability models ( p, q a,b φ ) used in GCL is defined by

Here, V is the set of nodes in the given graph, δ ij is the Dirac delta, τ n > 0 is a temperature parameter and sim( · , · ) denotes cosine similarity. This setting is often extended so that the definitions of ( p, q a,b φ ) vary according to how positive and negative pairs are sampled. Note that the target model q φ depends on the sampling method of augmentation operators, so the probability model also depends on ( a, b ) .

GCL trains the model using the following loss function on the pair of probability models ( p, q a,b φ ) induced by augmentation operations ( a, b ) , according to the formulation of contrastive learning loss (1).

This encourages the embeddings at the node level z a i and z b i of the same node under different 123 augmentation operations to be close to each other. Typically, augmentation operations a, b are chosen 124 by uniform sampling from a set of candidates A . Hence, in practice, the expected value is minimized 125 under the uniform distribution U A over A : 126

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While GCL achieves node-level representation learning via the procedure described above, in many cases the augmentation operations themselves rely on artificial perturbations such as randomly adding and/or deleting nodes and/or edges. In this study, we introduce gene knockdown-a biological perturbation-as supervision for these augmentation operations.

3.2 Notation and Problem Definition

In this study, we describe a GRN as a directed graph G /defines ( V , E , X V , X E ) that contains information on nodes and edges. Here, V , and E are the sets of nodes and edges, respectively, and each node represents a gene. X V i is the feature of the i -th gene, and X E i ∈ R |E| is the feature of the i -th edge in the network. The augmentation operation corresponding to the knockdown of the i -th gene is modeled by setting the feature of the i -th gene to zero and also setting the features of all edges connected to the i -th gene to zero.

We associate the a -th augmentation operation with the knockdown of the a -th gene. In what follows, we denote by G a the graph obtained by applying the a -th augmentation operation to G . Moreover, in this study, let H a be the teacher GRN for the knockdown of the a -th gene , and let K be the set of all augmentation operations for which such teacher GRNs exist. In other words, H a is a GRN that serves as a teacher for artificial augmentation for the a -th gene.

Our goal is to use the original GRN G and its teacher GRNs {H a } a ∈ K to train a Graph Neural Network (GNN) f φ . Defining embedded representations through the GNN f φ as

where z a i and y a i denote the embedding vectors of the i -th node in Z a and Y a , respectively, and d is the embedding dimension. Note that the same GNN f φ is used to produce both Z a and Y a .

In this work, we train the neural network f φ using the set of pairs { ( Z a , Y a ) } a ∈ K , where ( Z a , Y a ) corresponds to the graph embedding obtained by the GRN augmentation operation and the embedding of the teacher GRN for the corresponding gene knockdown.

4 Method

For the set of embedded representations { ( Z a , Y a ) } a ∈ K , we consider the pair of augmentation operations ( a, b ) and the pair of nodes ( i, j ) according to the contractive learning scheme. First, for the pair of augmentation operations ( a, b ) , we clarify the supervised learning problem for augmentation operations using KL divergence and then propose SupGCL using a distribution over pairs of combinations of nodes and extension operations. A sketch of the proposed method is shown in Figure 1.

The probability distribution of augmentation operations is naturally introduced by using similarities in the entire graph embedding space R |V| × d (rather than per node). By introducing the Frobenius inner product as the similarity in the matrix space, we define the probability models for the augmentation operations as:

where sim F ( · , · ) denotes the Frobenius inner product, and τ a > 0 is a temperature parameter. Unlike node-level learning, p φ ( b | a ) is not a fixed constant but rather a reference distribution based on the supervised embeddings { Y a } a ∈ K . Both probability models p φ and q φ are parameterized by the same GNN f φ .

Using these probability distributions, substituting the reference model p φ ( b | a ) and the target model q φ ( b | a ) into the formulation of contrastive learning in (1) yields the loss function for augmentation operations:

Minimizing this loss reduces the discrepancy in embedding distributions between the artificially 168 augmented graphs and the biologically grounded knockdown graphs. However, if both p φ and q φ 169

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Supervised

Augmentation Model

Node-level

Probabilistic model

Augmentation-Level

Probabilistic model

gmentation M

Node-Level GCL

Artificial Augmentation

Biological Augmentation

Graph Neural Network

Supervised Graph Contrastive Learning (SupGCL)

are optimized simultaneously, the model may converge to a trivial solution. For instance, if the GNN outputs constant embeddings, both distributions become uniform and Loss Aug = 0 . Thus, minimizing Loss Aug alone is insufficient for learning meaningful graph representations.

To address this issue, here we first introduce a reference model p φ ( j, b | i, a ) and a target model 173 q φ ( j, b | i, a ) that use conditional probabilities for each pair of node and augmentation ( i, a ) , ( j, b ) ∈ 174 V × K . By substituting these into the contrastive learning formulation in (1), we derive the loss 175 function of Supervised Graph Contrastive Learning: 176

Furthermore, the following theorem shows that by assuming independence between nodes and 177 augmentation operations in the reference distribution p φ , we can avoid the trivial solution: 178

Theorem 1. Assuming p φ ( i, j, a, b ) = p ( i, j ) p φ ( a, b ) , then 179

Proof: This follows directly from the standard decomposition of KL divergence: 180 D KL ( p ( x, y ) | q ( x, y )) = E x ∼ p ( x ) [ D KL ( p ( y | x ) | q ( y | x ))] + D KL ( p ( x ) | q ( x )) . See Appendix A 181 for details. 182

The first term in Theorem 1 corresponds to the expectation of the node-level GCL loss Loss a,b node 183 (as defined in Equation 3) with respect to the supervised augmentation distribution p φ ( b | a ) . This 184 allows node-level contrastive learning to reflect biological similarity between knockdown operations. 185 Importantly, since the theorem is independent of the specific choice of the node-level model ( p, q a,b φ ) , 186 any contrastive loss described by KL divergence can be used in practice. Meanwhile, the second term 187 reduces the distributional difference between the artificially generated augmentation-based GRN and 188

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Pre-trained GNN

Node-Level Task

Downstream Tasks

Graph-Level Task

[

Low risk

High risk

[

Subtype

] Subtype-Classification

Graph-Level Task

Breast only

[

Rel.

] Cancer Relation

[

BP.

] Biological Process

Node-Level Task

[

CC.

] Cellular Component

Figure 2: Overview of Downstream Tasks Used for Benchmark of GRNs Across Three Cancer Types: Breast, Lung, and Colorectal Cancers. The node-level tasks involve classifying genes into functional categories such as Biological Process [ BP. ], Cellular Component [ CC. ], and cancer relevance [ Rel. ]. The graph-level tasks include patient-level survival risk prediction [ Hazard ] and disease subtype classification [ Subtype ], the latter being specific to breast cancer. Mean pooling is applied to obtain graph-level representations.

the teacher GRN. Together, these two components ensure both expressive node representations and biologically meaningful augmentations.

Moreover, the performance of node-level representation learning and the biological validity following the teacher data for augmentation operations can be controlled by the temperature parameters τ n , τ a of each probability model. In particular, when the temperature parameter τ a involved in the augmentation operation is sufficiently large, the augmentation operation becomes independent of the teacher GRNs { Y a } a ∈ K , and coincides with the conventional node-level GCL loss function.

Proof: As τ a → ∞ , we have p φ ( b | a ) → U K ( b ) and q φ ( b | a ) → U K ( b ) . Therefore, the expectation term becomes: lim τ a →∞ E a,b ∼ p φ ( b | a )U K ( a ) [ Loss a,b node ] = E a,b ∼ U K [Loss a,b node ] , lim τ a →∞ D KL ( p φ ( b | a ) | q φ ( b | a )) = 0 thus proving the corollary.

In this study, we train the GNN using standard gradient-based optimization applied to the loss function defined in Theorem 1. The corresponding pseudocode is provided in Appendix B.

5 Experiments

In this study, we formulated SupGCL by naturally extending the loss function of conventional GCL, 203 based on a contrastive learning framework using KL divergence. Furthermore, we clarified the 204 relationship between SupGCL and conventional GCL through the temperature parameter. This 205 chapter verifies the effectiveness of the proposed method using actual gene regulatory networks 206 (GRNs) from cancer patients and augmented GRNs based on gene knockdown experiments. 207

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5.1 Benchmark of Gene Regulatory Networks

Evaluation Protocol: We evaluated the proposed method through the following procedure. First, based on gene expression data from cancer patients, we constructed patient-specific GRNs. Similarly, we constructed teacher GRNs using gene knockdown experiment data. Then, pre-training was performed on the proposed method using both the patient-specific and teacher GRNs. Subsequently, the performance of the downstream tasks, such as classification accuracy and regression performance, was evaluated using the pre-trained models and compared against comparative methods. Finally, we visualized the latent representations at both the node and graph levels extracted from the trained models.

We compared the proposed method with the following five comparative models:

w/o-pretrain : Directly performs classification or regression for downstream tasks without any pre-training.

GAE : [19]: Graph representation learning method based solely on graph reconstruction.

GraphCL : [13]: Graph contrastive learning using positive pairs between graphs.

GRACE : [15]: Node-level graph contrastive learning method.

SGRL : [17]: Node-level GCL that leverages representation scattering in the embedding space.

Datasets: To evaluate the performance of SupGCL, we conducted benchmark evaluations using real-world datasets. For constructing patient-specific GRNs, we used cancer cell sample data from The Cancer Genome Atlas (TCGA). For constructing teacher GRNs, we used gene knockdown experiment data from cancer cell lines in the Library of Integrated Network-based Cellular Signatures (LINCS). The TCGA dataset [20] and the LINCS dataset [21] are both large-scale and widely-used public platforms providing gene expression data from cancer patients and cell lines, respectively.

We used normalized count data from the TCGA TARGET GTEx study [22] provided by UCSC 230 Xena. For LINCS, we used normalized gene expression data from the LINCS L1000 GEO dataset 231 (GSE92742) [23]. 232

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Graph Level

GraphCL

GRACE

SupGCL

SGRL

GAE

Basal

Her2

Luminal A

Luminal B

Normal

t-SNE1

t-SNE2

Metabolism

Signal-transduction

Cell Component Transduction

t-SNE1

t-SNE2

Experiments were conducted for three cancer types across both datasets: breast cancer, lung cancer, and colorectal cancer. Furthermore, the set of genes constituting each network was restricted to the 975 genes common to the TCGA gene set and the 978 LINCS landmark genes. The number of patient samples for each cancer type was N=1092 (breast), 1011 (lung), and 288 (colorectal), and the total number of knockdown experiments was 8793, 15926, and 11843, respectively. The number of unique knockdown target genes / total common genes was 768/975, 948/975, and 948/975.

The TCGA dataset also includes survival status and disease subtype labels associated with each gene expression profile. Additionally, each gene was annotated with multi-labels based on Gene Ontology [24] - Biological Process (metabolism, signaling, cell organization; 3 classes), and Cellular Component (nucleus, mitochondria, ER, membrane; 4 classes). We also used the OncoKB [25] cancer-related gene list to assign binary relevance labels. These labels were used for downstream tasks. Details are provided in Appendix C.

Pre-processing: To estimate the network structure of each GRN from gene expression data, we used a Bayesian network structure learning algorithm based on nonparametric regression with Gaussian noise [26]. For each experiment, gene expression values were used as node features, while edge features were defined as the product of estimated regression coefficients and the parent node's gene expression [27]. This structure estimation was performed per cancer type per dataset using the above algorithm. Further details are provided in Appendix D.

5.2 Result 1: Evaluation by Downstream Task

In this experiment, pre-training of the proposed and conventional methods was conducted using patient-specific GRNs from TCGA and teacher GRNs from LINCS. Subsequently, fine-tuning was performed on the pre-trained models using patient GRNs, and downstream task performance was evaluated (see Figure 2). During fine-tuning, two additional fully connected layers were appended to the node-level representations and graph-level representations (obtained via mean pooling), and downstream tasks were performed.

Graph-level tasks (hazard prediction, subtype classification) used survival and subtype labels from TCGA. Note that subtype classification was conducted only for breast cancer. Node-level tasks (Biological Process - BP, Cellular Component - CC, and cancer relevance - Rel.) used gene-level annotations from Gene Ontology and OncoKB.

Details of these downstream tasks are summarized in Table 1. 262

Each downstream task - hazard prediction, subtype classification, BP, and CC classification - was 263 evaluated using 10-fold cross-validation. For cancer gene classification, due to label imbalance, we 264 performed undersampling over 10 random seeds. Results are reported as mean ± standard deviation. 265

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For all methods including the SupGCL and conventional methods, we used the same 5-layer Graph Transformer architecture [28]. Hyperparameters for pre-training were tuned with Optuna [29], and the model was optimized using the AdamW optimizer [30]. All training runs were performed on a single NVIDIA H100 SXM5 GPU. Additional experimental details can be found in Appendix E.

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Tables 2 and 3 show the results for node-level and graph-level tasks, respectively. The best performance is indicated in bold, and the second-best is underlined. Although SupGCL did not achieve statistically significant superiority in every single task, it consistently outperformed other pre-training methods across all datasets and tasks.

For node-level tasks, many existing methods did not show much improvement over without-pretrain, whereas SupGCL consistently demonstrated strong performance. This suggests that SupGCL effectively captures biologically meaningful GRN representations suitable for these tasks. In graph-level tasks like hazard and subtype prediction, while some existing methods showed marginal improvement over without-pretrain, SupGCL achieved significantly higher performance.

5.3 Result 2: Latent Space Analysis

Using the pre-trained models, we visualized the embedding spaces derived from the breast cancer dataset (see Figure 3). Other similar visualizations are shown in Appendix F.

The top row of Figure 3 shows the node-level embeddings. Colors represent one of the three singlelabel Biological Process annotations (metabolism, signaling, or cell organization). Across all models, no clear clustering was observed based on labels. However, GraphCL's embeddings were notably different from others, with signs of latent space collapse. Detailed observations are provided in Appendix F. This suggests that GraphCL may be more suited to graph-level discrimination than node-level representation.

Compared to GraphCL, the other models showed more dispersed embeddings, though without distinct clustering.

The bottom row of Figure 3 illustrates the graph-level embeddings, colored by disease subtype. From this, it is evident that GAE and GraphCL fail to separate subtypes in the embedding space. In contrast, GRACE and SGRL showed moderate separation between the Basal subtype and others. SupGCL displayed the clearest separation, indicating its ability to better learn subtype-specific network representations.

6 Conclusion

In this study, we proposed a supervised graph contrastive learning method, SupGCL, for representation learning of gene regulatory networks (GRNs), which incorporates real-world genetic perturbation data as supervision during training. We formulated GCL with supervision-guided augmentation selection within a unified probabilistic framework, and theoretically demonstrated that conventional GCL methods are special cases of our proposed formulation. Through benchmark evaluations using downstream tasks based on both node-level and graph-level embeddings of GRNs from cancer patients, SupGCL consistently outperformed existing GCL methods.

A limitation of this paper is that the effectiveness of the proposed method has only been confirmed in situations where the teacher GRNs were constructed from knockdown experiments of the same cancer type.

As future work, we plan to expand the target cancer types and develop a large-scale, general-purpose SupGCL model that can operate across multiple cancer types.

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Peer reviews:
comments: SUMMARY:
This paper focuses on gene regulatory networks and aims to pretrain a model that can be effectively adapted to various downstream tasks. Specifically, it transfers supervised graph contrastive learning methods to this domain. Experimental results demonstrate the usefulness of the pretraining approach.

STRENGTHS AND WEAKNESSES:
Pros：

1.The study of gene regulatory networks is of significant scientific importance.


Cons：

1.The paper lacks novelty. It appears to explore a generic self-supervised graph representation learning method, as all the components used in the proposed approach are derived from existing and well-established graph contrastive learning (GCL) frameworks. Moreover, all baseline models used for comparison are classical GCL methods, with no analysis or comparison involving GRN-specific approaches. This makes it difficult to assess the fairness and significance of the reported performance gains, as the compared methods operate under different assumptions and levels of domain-specific modeling.

2.The related work section is insufficient. It lacks an in-depth discussion of prior research on GRNs, leaving readers unclear about the specific challenges in this domain. Additionally, the paper mentions that graph augmentations may alter the topology, but this issue has already been widely discussed and addressed in many prior works.

3.The manuscript contains redundant content in the background section. It is recommended to streamline this part and consider following the more concise formulation used in works like NT-Xent.

QUESTIONS:
See above.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 2
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-06-12

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a novel graph contrastive learning (GCL) framework, SupGCL, specifically designed for the graph representation learning of gene regulatory networks (GRNs). SupGCL addresses a key challenge encountered when applying GCL methods to GRN representation learning: artificial perturbations can disrupt the heterogeneous characteristics of GRNs, thereby hindering the learning of effective graph representations. SupGCL constructs a Supervised Augmentation Model to impose constraints on the reference distribution in the loss function, and performs representation learning from both the node level and the graph level, thereby enhancing the overall learning performance. Experimental results demonstrate that SupGCL consistently outperforms conventional graph contrastive learning approaches across all downstream tasks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The authors consider a biologically motivated augmentation method, gene knockdown, and use the resulting augmented graphs to construct positive and negative sample pairs for graph contrastive learning.\n\n2. The authors model the probability distribution from the perspective of the entire graph embedding space and integrate the reference model with the target model. This constitutes one of the key innovations of the work, proposing a novel paradigm for graph contrastive learning.\n\n3. To prevent model collapse, where the model may converge to a trivial solution, the authors theoretically show that introducing a conditional probability distribution and assuming independence between nodes and augmentation operations in the reference distribution can effectively avoid such trivial solutions.\n\nWeaknesses:\n1. Although the authors propose the Supervised Augmentation Model, they do not provide ablation experiments to validate its effectiveness.\n\n2. The motivation behind proposing the Supervised Augmentation Model is not clearly explained. Merely justifying it from the perspective of retaining biological characteristics is insufficient; the authors should also elaborate on why modifying the reference distribution in traditional GCL is necessary.\n\n3. The temperature parameter in contrastive learning typically has a significant impact on model performance. However, the authors lack a thorough analysis of key hyperparameters; for instance, they do not present experiments with multiple settings to illustrate how the temperature parameter affects model performance.\n\n4. When selecting baseline methods for comparison, the authors do not include state-of-the-art GNN-based approaches specifically designed for gene regulatory network (GRN) tasks. Moreover, the selected methods, such as GAE and GraphCL, are relatively outdated, and the number of comparison methods is also insufficient, leading to a lack of convincing experimental validation.\n\n5. Regarding Result 2, the experimental results provided by the authors do not clearly demonstrate the superiority of SupGCL. More compelling evidence should be presented to show that the latent representations learned by SupGCL are of higher quality than those obtained by the comparison methods. It is difficult to assess the quality of the learned latent representations based solely on visual inspection. The authors are encouraged to include more quantitative metrics to enhance the objectivity and persuasiveness of the evaluation.\n\nQUESTIONS:\n1. Regarding the avoidance of the trivial solution, I would like to ask whether you have considered adopting strategies similar to those used in SimSiam or MoCo to prevent model collapse, such as the stop-gradient technique or the momentum encoder.\n\n2. Did you study how the amount of pretraining data affects model performance? Does using more data always lead to better results?\n\n3. In addition to node-level and graph-level downstream tasks, have you considered using edge-level tasks to evaluate the model's performance? For example, link prediction tasks such as GRN inference.\n\n4. For the comparison methods, why did you choose to change the model architecture to Graph Transformer instead of keeping the original network structure, such as GCN or GAT in GRACE.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes SupGCL for learning biologically meaningful representations of GRNs. It improves standard GCL by supervising artificial graph augmentations (simulated gene knockdowns) with real biological data from experimental knockdowns. Using a shared graph neural network, it aligns node-level and graph-level embeddings between fake and real knockdowns via KL-divergence-based contrastive losses, enabling more faithful modeling of gene interactions.\n\nSTRENGTHS AND WEAKNESSES:\n***Strengths***:\n1. SupGCL uses actual gene knockdown data to guide how the graph is augmented, instead of just applying random or hand-crafted changes. This makes sense from biological perspective.\n2. The method combines losses that look at both individual nodes and the whole augmented graph using perturbation data. This, to my knowledge, is novel use of perturbation data to design augmentations for GCL.\n\n***Weaknesses***:\n1. The paper claims that the propose augmentation strategy helps improve biological realism. Is it possible to verify this more directly other than using the results  on downstream tasks to support this hypothesis.\n2. SupGCL assumes all the knockdown graphs are equally reliable, but in biological contexts some knockdown experiments may have more relevance than others. Can the framework handle such scenarios?\n3. Since the model learns to match artificial and real knockdowns, is it possible that it can learn/memorize batch effects from knockdown data in case they all come from a few cell lines?\n\nQUESTIONS:\nExpanding on the weaknesses described above, I propose the following questions which the authors may consider answering.\n\n1.  It would be good to do some analysis where you cluster the embeddings and see if those clusters line up with known biological groups or gene ontology terms.\n2. Can we add a way to weight the knockdown graphs based on how trustworthy they are, or filter out the noisy ones?\n3. Is it possible to check how well the model works on new genes or cell types and if there are any risks of overfitting to knockdown data from few cell lines?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes SupGCL, a new supervised graph contrastive learning method tailored for gene regulatory networks (GRNs). Traditional graph contrastive learning approaches typically rely on artificial perturbations such as random node or edge removal for data augmentation, which are often biologically implausible when applied to GRNs. In contrast, SupGCL leverages real biological perturbations by incorporating gene knockdown experiment data as supervision to guide the learning of graph representations. The authors formulate this approach within a probabilistic KL divergence-based framework, demonstrating that existing unsupervised GCL methods are special cases of their formulation when supervision is removed. They apply SupGCL to GRNs constructed from cancer patient data and gene knockdown experiments across breast, lung, and colorectal cancer datasets. The model is evaluated on various downstream tasks, including gene function classification (node-level tasks), patient hazard prediction, and disease subtype classification (graph-level tasks). Experimental results show that SupGCL consistently outperforms state-of-the-art baselines in both node- and graph-level evaluations, suggesting its effectiveness in learning biologically meaningful GRN representations that can enhance predictive performance in biomedical applications.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\nThe paper demonstrates strong technical quality. It provides a clear mathematical formulation of SupGCL as a probabilistic extension of conventional GCL, incorporating supervised biological perturbations (gene knockdowns). The theoretical connection showing that existing GCL methods are special cases of SupGCL is rigorous and well-motivated. Empirical results are comprehensive, covering both node-level and graph-level tasks across multiple cancer types with meaningful biological datasets (TCGA and LINCS). The experimental setup, including pre-training and fine-tuning protocols, is sound, and performance improvements are consistently demonstrated over strong baselines such as GraphCL, GRACE, and SGRL.\n\nThe significance of this work is high. It addresses an important problem in biological network representation learning: the lack of biologically meaningful data augmentations in GCL, which limits applicability to real biological tasks. By incorporating gene knockdown data, SupGCL enables learning representations that are more aligned with biological realities, potentially benefiting downstream biomedical tasks such as disease subtyping, hazard prediction, and functional gene annotation. This approach could inspire broader integration of supervised perturbations in contrastive learning for other structured biological or clinical data domains.\n\nWeaknesses:\nAlthough the paper is technically solid, some details could be improved. \n\nWhile the authors demonstrate performance gains, statistical significance testing across methods is not thoroughly discussed, which would strengthen claims of superiority. \n\nHyperparameter choices for SupGCL versus baselines are briefly mentioned but could be detailed further to rule out tuning bias. \n\nWhile Theorem 1 is proved, the practical intuition behind some derivations (e.g. KL decomposition under independence assumptions) could be elaborated for accessibility.\n\nA limitation in significance is that the evaluation is restricted to knockdown perturbations from the same cancer type as the patient GRNs. While the authors acknowledge this, it constrains the generalizability claims of SupGCL. \n\nPractical integration into clinical pipelines remains speculative and is not discussed, which would strengthen its real-world relevance.\n\nThe paper assumes substantial familiarity with GCL literature and probabilistic formulations, which may limit accessibility to readers from computational biology backgrounds who are potential beneficiaries of this work. Certain sections, especially the derivation-heavy Method section, could benefit from intuitive summaries before formal equations to maintain readability.\n\nWhile the incorporation of supervised biological perturbations is novel, the contrastive learning formulation itself is an extension rather than a fundamentally new learning paradigm. The broader originality is therefore dependent on the biological application rather than the machine learning method itself.\n\nQUESTIONS:\nThe proposed SupGCL framework uses gene knockdown data from the same cancer type as supervision. How does the method perform if knockdown data from other cancer types are used, or if certain knockdowns are unavailable for a target cancer type?\n\nWhile the tables report means and standard deviations, were statistical significance tests conducted to compare SupGCL against baselines on downstream tasks?\n\nThe paper mentions that hyperparameters were tuned with Optuna, but it is unclear whether all baseline models underwent equivalent tuning efforts.\n\nBeyond t-SNE visualizations, are there biological interpretations or validation of the learned embeddings, such as enrichment analyses or biological pathway coherence for gene-level representations?\n\nGiven the paper’s motivation around clinical and therapeutic applications, what are the practical considerations or limitations for deploying SupGCL in real clinical genomics pipelines (e.g. computational cost, data availability, required supervision)?\n\nLIMITATIONS:\nThe discussion of potential negative societal impacts could be expanded. For example, while the method advances biological understanding, its use in clinical decision-making without rigorous validation could risk misinterpretation of patient-specific predictions. Additionally, there could be implications regarding equitable access to such computational tools in genomics.\n\nIncluding a brief reflection on these societal aspects would strengthen the ethical transparency of the paper without detracting from its scientific focus.\n\n\n\n\n\nAsk ChatGPT", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-04"}