dataesr/scientific-paragraphs-categorization huggingface dataset:
Genes and variants were annotated with VEP (Variant Effect Predictor). Oncogenic and clinically actionable mutations were identified with PCGR (Personal Cancer Genome Reporter). RNA-seq data were analyzed by the Kallisto v0. 46. 2 18, and Sleuth v0. 30. 019 recommended pipelines. ctDNA was sequenced by the Thermofisher Oncomine Pan-Cancer Cell-free assay (Pan-Cancer). Alternatively, tDNA and ctDNA sequencing was outsourced (Foundation One CDx, Foundation One CDx, and Foundation One Liquid). Reverse Transcription PCR (RT-PCR) and dgital PCR (dPCR) were carried out on either or both tDNA and ctDNA using the Thermofisher QuantStudio 3D and custom-designed assays, as described  [16].
Annotation and recording Patients with germ line variants were referred to our genetic counseling unit. The IRE MTB focused on patients with actionable somatic alterations (referred to as actionable alterations hitherto) from the MTB and routine NGS groups. These were recorded in a dedicated webapp, called Molecular Tumor Board Orchestrator (MTBO), that will be the subject of a separate report. Briefly, it was developed (frontend and backend) in Visual Basic with an SQL server database handling/recording meeting calls, email announcements, molecular testing/ annotation, outcomes, and the final MTB Report.
MTB workflow and attendance model The IRE MTB operational workflow involves 6 steps, from informed consent to therapy assignment (Fig. 1 ) with a strictly enforced turnaround time of 15 days from case presentation to recommended therapy (e. g. between the 2 discussion rounds, step 2 to step 5). The entire workflow and patient outcomes are real-time annotated and traceable by the dedicated MTBO webapp. Active members and main attendants (Fig. 1 , step 2) include oncologists, molecular biologists/biotechnologists, pathologists, biostatisticians and bioinformaticians, a hospital pharmacist, a delegate from the Institutional Biobank, the Scientific Secretary and an executive Secretary. Hematologists, endocrinologist-oncologists, a medical geneticist, interventional radiologists and radiotherapists, surgeons, patient advocacy delegates, nurses, data managers and representatives of the local health governance are invited to participate depending on the topics being addressed, as per the MTB agenda distributed at least 24 h in advance.
Specific software for ML applications in microbiome studies In Supplementary Table  1  we summarize the most commonly used ML software for microbiome data analysis including the applicability (one application or more), availability of source code, last version, number of citations based on the Scopus database (this gives an idea about the level of usage), type of tool (level of deployment) and availability (public/commercial) for all the software and tools included. Each publication has been associated with the URL (pointed in the text) to the software described therein. 1 Next, the software was evaluated in terms of the technologies used and the main ML tasks performed by the software. This allowed us to verify the most common ML tasks, the technologies used, and the change in the technologies used in recent years.
The last remark concerns the availability of the software after years, most likely due to the academic funding and career structure. Our observations show that as much as 11. 5% of projects created between 2005 and 2022 are no longer maintained 2 -and the software can only be found in the Internet Web archive. In Figure  2  we present a series of specialized ML software and tools used to facilitate several microbiome research steps. These steps include feature generation, where raw 16 s rRNA and shotgun sequencing data are processed and transformed into interpretable microbial units; data integration, where disparate datasets are combined for comprehensive analysis; and feature analysis, where a variety of tools are employed to perform time series analysis, gene prediction, metabolic modeling, disease prediction, and comparative metagenomics.
These software and tools, discussed in detail in the next sections, can empower researchers to uncover the intricate dynamics within microbiomes and advance their understanding of their roles in human health. The emphasis is on ML software, and hence quite a number of very popular software in microbiome studies (Metaphlan, KneadData, and Kraken2,) would not be mentioned, due to omitting ML approaches. Furthermore, we provide a comprehensive interactive table in the Supplementary materials that summarizes available software and tools for analyzing different types of microbiome data, organized according to their primary application (code accessible at https://github. com/ laurichi13/Toolbox-ML-software).
Feature generation and taxonomic assignment from 16S rRNA gene sequencing Human (and environmental) microbial analyses are often performed using 16S rRNA gene sequencing. This is possible as the 16S rRNA gene is highly conserved and universally present across prokaryotes. The 16S rRNA gene analysis implies using primers to amplify the hypervariable regions of the 16S rRNA gene (ranging from V1 to V9; frequently targeted for bacteria are the V3, V4, and V3-V4 regions;  Nguyen et al. , 2016). Amplicon Sequence Variants (ASVs) provide a precise resolution of sequence variations without imposing arbitrary dissimilarity limits, unlike Operational Taxonomic Units (OTUs), which are commonly used in 16S rRNA data processing  (Eren et al. , 2013). ASV techniques utilize Illumina-scale amplicon data and can identify sequence differences as small as one nucleotide.
, 2022) , where the algorithm gathers exact sequence variants de novo with little room for mismatches and determines their abundance. Based on the inferred ASVs, an error model is calculated for the dataset to compare highly similar reads in order to statistically exclude sequencing errors. This is based on the assumption that true biological sequences occur in higher frequencies than sequences emerging from sequencing errors. Moreover, unlike de novo clustered OTU, the identity of an ASV keeps its validity outside of the data set from which it was derived, thereby also simplifying meta-analyses of multiple data sets  (Callahan et al. , 2017). However, some limitations inherent to OTU-based methods such as multiple copies of the target region within an organism (e. g. , 16S rRNA gene copy numbers) and the restricted information content of short reads also apply to ASV-based methods and should be considered in the interpretation of results.
, 2013). Once a hierarchical tree is constructed, the meaningful clusters can be defined by cutting the tree at a user-specified similarity threshold and merging all the sequences with higher similarity in the same OTU. Among these methods, the most familiar ones are Dotur  (Schloss and Handelsman, 2005) , based on Multiple Sequence Alignments, Mothur  (Schloss et al. , 2009) , based on Needleman-Wunsch alignments against a pre-aligned reference database and ESPRIT  (Sun et al. , 2009) , which implements a complete-linkage hierarchical clustering and minimizes the memory usage by adopting a k-mer distance for faster identification of very similar sequence pairs, producing sparse distance matrix. In hierarchical approaches, the number of sequences to be compared (N) determines the computational complexity [O(N 2 )], which usually renders these approaches more intensive as stated by the authors.
, 2001; Li and Godzik, 2006). GramCluster  (Russell et al. , 2010)  indexes the input dataset by a suffix tree for efficiency. Uparse  (Edgar, 2013) , an improvement of USEARCH  (Edgar, 2010)  and OTUCLUST  (Albanese et al. , 2015)  rely on high quality sequences only, including steps for quality filtering, trimming, and chimera filtering. Swarm  (Mahé et al. , 2014)  uses an agglomerative, unsupervised, single-linkage clustering algorithm that avoids the use of a global threshold. Each amplicon can be seen as a point in the discrete amplicon space, where its nearest neighbours have one nucleotide difference. User set parameter d is considered a tolerable similarity threshold, so that d-neighbours in the amplicon space are all amplicons with d nucleotide differences. Clustering amplicons starts from a seed, collecting all of its d-neighbours, and continues iteratively from these subseeds until natural cluster limits are reached, where no d-neighbours of any subseed can be added.
In such a discrete amplicon space, amplicon clusters (OTUs) should be clearly separated contiguous regions, and the procedures ensures that all similar amplicons (i. e. , amplicons close in the space) belong to the same cluster. DNACLUST  (Ghodsi et al. , 2011)  adopts a greedy approach but improves the speed using filtering based on k-mers. There is an open-source 64-bit program VSEARCH  (Rognes et al. , 2016)  which can be used instead of USEARCH, for which the source code and 64-bit versions are not publicly available.
Bayesian methods The RDP classifier  (Wang et al. , 2007; Cole et al. , 2009)  relies on a reference sequence database that contains relevant species, and then assigns a class label to each read by the naïve Bayesian algorithm based on k-mer occurrence. Moreover, we can find NBC  (Rosen et al. , 2011)  and the classifier FCP  (Parks et al. , 2011) , which also implement a naïve Bayesian framework. pplacer  (Matsen et al. , 2010) , is a software package for phylogenetic placement and subsequent visualization, which offers a full probabilistic and Bayesian framework to locate a query sentence in a reference phylogeny so that a taxon identifier can be assigned to the query sequence. Through QIIME2  (Bolyen et al. , 2019)  plugin q2-feature-classifier  (Bokulich et al. , 2018a) , it is now also possible to train an almost arbitrary classifier from the Python library Scikit-learn and use it to predict the taxonomy.
The real shift in taxonomic assignment came with  (Kaehler et al. , 2019) , when the increase in the species-level classification accuracy is achieved by incorporating environmentspecific taxonomic abundance information. Classifiers for amplicon sequences, like Naive Bayes, assume that all species in the reference database are equally likely to be observed  (Kaehler et al. , 2019). However, in practice, the equal probabilities (or the uniform weights) assumption is not fulfilled resulting in reduced accuracy. As the authors explain  (Kaehler et al. , 2019) , the accuracy is less if weight distribution is closer to uniform than if it is further. In QIIME2 it is implemented as a preprocessing step through its plugin q2-clawback. The plugin is used for assembling taxonomic weights, which are further used as input into taxonomic classification.
These models are computationally expensive but often have high accuracy, and are good at capturing complex biological systems. TAC-ELM  (Rasheed and Rangwala, 2012 ) is a composition-based method that uses a neural network-based model. LookingGlass  (Hoarfrost et al. , 2022)  is a deep learning biological language model designed to capture the functional diversity of the microbial world by encoding contextually aware representations of short DNA reads. The model takes into account the order in which sequences appear and thus produces contextually relevant embeddings.
Comparative metagenomics This section includes techniques that label entire samples by examining features derived from each amplicon or shotgun DNA fragment from the sample (k-mers or OTU/ASV frequencies), sometimes supplemented with additional information (e. g. , metadata, phylogenetics, class labels etc. ). A common application of this classification in biomedical settings is phenotype analysis based on metagenomic fragments  (Soueidan and Nikolski, 2016). MetaPhyl  (Tanaseichuk et al. , 2014 ) is a two-phase heuristic algorithm for separating short paired-end reads from different genomes in a metagenomic dataset. The algorithm is based on the observation that most of the l-mers belong to unique genomes when l is sufficiently large. In the first stage of the algorithm, groups of l-mers are produced, each of which is associated with a single genome.
g. , environmental, culture, and host conditions). All statistical analyses are performed by combining and normalizing user-submitted taxonomic profile data and automatically mapped phenotypic information (e. g. , oxygen requirements, temperature range, habitat, host type, pathogenicity, disease association etc. ) from METAGENassist's phenotypic database. A variety of univariate methods are available for feature ranking regarding the significance of their changes due to the different conditions under study (e. g. , fold change analysis, t-tests, Mann-Whitney tests, ANOVA, Kruskal-Wallis tests). Multivariate methods, namely, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), can be used for dimension reduction, visualization, classification, and feature identification.
Hierarchical and partitional clustering methods are available to identify groups of samples regarding their feature abundance profiles, given their similarity based on a defined distance measure. For the prediction of attribute labels and the identification of important features (i. e. , taxa or mapped phenotypes) METAGENAssist offers two methods, random forest and recursive SVM feature selection and sample classification (R-SVM). Mian  (Jin et al. , 2022)  is another interactive web-based microbiome data table visualization and ML platform. Users can upload their metagenomic data as well as accompanying metadata, taxonomic mappings, phylogenetic tree or gene expression data. Mian allows users to preprocess their data, calculate alpha and beta diversity measures, apply feature selection methods and train ML models such as linear regressors, random forest or multilayer perceptrons.
All tools are easy to tune and configure, and users will also be able to obtain common statistical measures as well as different plots for data visualization. MetaDistance  (Liu et al. , 2011 ) is a MATLAB toolbox that comprehends the relationship between clinical phenotypes and microbiota profiles by developing new supervised learning tools. Instance-based [K-Nearest Neighbors (KNN)] and model-based (SVM) learning techniques have been combined to create the sparse distance learning approach (MetaDistance) that the authors have proposed for multi-class classification. The suggested approach is capable of class prediction and taxon identification in tandem. It can perform multiclass classification while not exacerbating any existing class imbalance. Additionally, this approach estimates only a few parameters, and specifically, the number of these parameters is equal to the number of features (input variables) in the dataset.
Evaluation metrics commonly used to measure the model's performance include accuracy, sensitivity, specificity, precision, F1 score, AUC, among others (Table  1 ). MetAML has been tested on metagenomic case-control datasets from five different diseases, demonstrating potential for  disease detection from gut metagenomic data. It has also been used in a study by  Thomas et al. (2019) , where ML models based on MetAML were developed to predict colorectal cancer using metagenome dataset. The models evaluated the prediction accuracies of the gut microbiome for colorectal cancer detection across populations and successfully identified consistent microbiome biomarkers and accurate disease-predictive models. PopPhy-CNN  (Reiman et al. , 2020 ) is a convolutional neural network (CNN) that predicts the host's disease status using their microbiome samples.
PopPhy-CNN involves transforming the phylogenetic tree and microbial abundance data into a structured matrix format. This matrix, enriched with evolutionary information, is then used as input for a CNN model to make predictions about the host's disease status. The incorporation of biological knowledge through this process contributes to the model's superior performance compared to other methods in binary classification and multi-class datasets. PopPhy-CNN models were more competitive than RF, SVMs, LASSO, 1D-CNN, MLPNN, and Ph-CNN models across nine moderately sized metagenomic datasets for binary classification  (Qin et al. , 2012 (Qin et al. , , 2014;; Karlsson et al. , 2013; le Chatelier et al. , 2013; Sokol et al. , 2017). According to authors, PopPhy-CNN can deliver reliable performance with minimal training data and shows the best results for multi-class biological and synthetic datasets.
Accuracy Met2Img  (Hai Nguyen et al. , 2019 ) is a disease prediction method that uses Synthetic Image Representations of Metagenomic data and CNN. The authors use a rectified linear unit (ReLU) activation function and transform each sample into an image containing coloured pixels representing the microbes and their relative quantities. The resulting images are subsequently used as features for the neural network. The authors evaluated the method using six metagenomic datasets, including five disease types and more than 1,000 samples. They reported encouraging results and held applicability across diverse omics data scenarios, including integrative contexts (i. e. , taxonomic levels, CNN structure optimization, dimensionality reduction: effective colormaps, and GPU efficiency). RegMIL is a Multiple Instance Learning (MIL) method that predicts phenotypes from metagenomic data.
This approach employs a rapid, hash-based clustering technique referred as Canopy clustering to score instances in the training set. These scores estimate the contribution of an instance (sequence) to the disease. The instance scores of the training set are used to train a two-layer neural network-based regression model to score instances in the test set. In the end, one histogram-based bag-level feature representation by taking contributions of each instance to train a classifier  (Rahman and Rangwala, 2018). RegMIL was shown to predict a person's health status with high accuracy when evaluated with liver cirrhosis and IBS datasets, outperforming other tools like MetAML  (Rahman and Rangwala, 2018). mAML is an automated ML tool specifically designed for classification tasks performed on metagenomic data.
The tool was developed in Python and the entire pipeline can be run through a web server, although it is also available to download and run locally. mAML preprocesses the data, performs grid-search for hyperparameter tuning, and provides several performance metrics for the classification task set by the user. The web-based tool allows the user to personalize each of these tasks. The mAML pipeline exhibits various benefits: (i) it can effectively and automatically construct an optimized, interpretable and resilient model for a microbiome-based classification task; (ii) it is implemented on a web-based platform (the mAML web server); (iii) the pipeline can be employed for both binary and multiclass classification tasks; (iv) it is data-driven and can readily be extended to encompass multiomics data or other data types, given the availability of domain specific datasets  (Yang and Zou, 2020).
The authors evaluated mAML on 13 different metagenomic datasets, including binary and multi-class data. The models generated by mAML outperformed other models such as Support Vector Classifiers or logistic regression  (Fierer et al. , 2010; Wu et al. , 2011; Qin et al. , 2014; Montassier et al. , 2016) , demonstrating the method's robustness. This method has been applied to predict carboxylate production from 16S rRNA gene dynamics  (Liu B. et al. , 2022). DeepMicro is a deep learning method that is focused on the extraction of features from high dimensional microbiome data (more specifically extracted abundance and strain profile). It was shown to be more accurate than MetAML in transforming high-dimensional metagenomic data into a reliable low-dimensional representation for supervised or unsupervised learning  (Curry et al.
MetaNN (Lo and Marculescu, 2019) is a neural network-based technique that addresses challenges related to over-fitting and high dimensionality in metagenomic data, leading to improved classification accuracy. The method involves removing taxa that appear in less than 10% of the samples and generating additional samples using a negative binomial distribution to augment the training set. A neural network is then trained on the augmented dataset, resulting in superior performance compared to other ML models such as Random Forests, SVM and CNN, as demonstrated in evaluations by the authors Lo & Marculescu in 2019 using both synthetic and real datasets. SIAMCAT is an R-based software that combines ML, statistical modeling, and advanced visualization approaches to enable comparative metagenomic studies.
The tool provides normalization methods, cross-validation schemes, and implementation of various ML approaches such as LASSO  (Tibshirani, 1996) , Elastic Net  (Zou and Hastie, 2005) , and RF  (Ho, 1995) , among others. The trained models can then be used to make predictions based on the provided metagenomic data, and their performance can be measured using AUROC. According to  Wirbel et al. (2021) , SIAMCAT allows users to apply robust and verified ML models to their datasets, allowing pre-processing and normalization of the datasets depending on metagenomic data properties. It has been used in various studies, including those involving the classification of oral microbiome data   (Lee et al. , 2022). In the study developed by  Kartal et al. (2022) , it was discussed if fecal and salivary microbiota could be used as predictors of pancreatic ductal adenocarcinoma.
The resultant subset of vectors is then employed to establish an LDA model that ranks the features based on their relative differences among classes. Ultimately, the output is a list of discriminative features that are in line with the subclass grouping within classes and are ranked based on their effect size in distinguishing between classes. MarkerML is a web server that employs interpretable ML and statistical testing to discover important metagenomic features  (Nagpal et al. , 2022). Its main goal is to identify marker-features, which can contrast comparable states and help in decision-making. Model interpretability is achieved by incorporating Shapley Additive exPlanations (SHAP)-based  (Lundberg and Lee, 2017)  analyses to detect predictive marker features. MarkerML also implements statistical testing methods to contextualize marker-feature discovery in metagenomic datasets, such as ANCOM-BC  (Lin and Peddada, 2020; Lin et al.
, 2022)  or ALDEx2  (Fernandes et al. , 2013 (Fernandes et al. , , 2014;; Gloor et al. , 2016). It also offers features such as access to databases (e. g. , Taxonomic, KEGG, COG, PFAM), normalization options, feature selection, and multiple ML algorithms (e. g. ,  XGBoost, Random Forests, Logistic Regression; Nagpal et al. , 2022). MarkerML relies on class comparison and prediction for biomarker discovery, achieved by analyzing differential abundance and ML techniques, respectively. Selbal is an algorithm whose objective is to find a microbial signature, i. e. , a model defined by a group of microbial taxa whose pattern of abundance is predictive or associated with an outcome variable of interest  (Rivera-Pinto et al. , 2018). It uses the Selbal model selection method to find two groups of taxa whose relative abundance (referred as "balance") sufficiently explains the target response variable  (Rivera-Pinto et al.