dataesr/scientific-paragraphs-categorization huggingface dataset:
With the maSVM approach, we show for the first time that the automatic classification of large quantitative interaction datasets can be performed with high confidence. Notably, such a classification strategy has been successfully used to predict kinase substrates from phosphoproteomics data  (Yang et al, 2019; Kim et al, 2021) , or to classify cell types from single cell RNA-sequencing  (Abdelaal et al, 2019). We show that the maSVM algorithm can reduce the variability between recovered interactions in different experiments, and provide probabilities for being a true interaction for every protein pair tested. For example, we observed that in the LuTHy-BRET and mN2H assays, one pair from the random reference set hsRRS-v2 (SLC6A1+TM4SF4) was classified as a true-positive interaction with >99% probability (Figure  1J ).
Due to its definition as a negative or random interaction, a traditional scoring approach would have classified this pair as strictly negative and thus classify similar or lower scoring pairs also as negative. However, it is very likely that in the LuTHy-BRET and mN2H assays, these two proteins interact biophysically when overexpressed in HEK293 cells, making the pair a potential pseudo-interaction, i. e. a true biophysical interaction without in vivo biological relevance  (Braun et al, 2009). The maSVM algorithm is able to deal with such exceptions and thus increases robustness in scoring PPIs even when inconsistencies among assays or in reference sets are present. We also show that the maSVM algorithm is universally applicable to classify binary PPIs from different quantitative datasets, including the interface analysis of AFM-predicted complex structures.
Thus, it provides a framework to directly compare the results of various binary interaction assays and in silico predictions, which will lead to increased reproducibility and interpretability of results between experiments and methods. We have further expanded the use of the unbiased maSVM algorithm scoring approach to map binary PPIs in three established multiprotein complexes, and to identify potential targets for drug discovery among SARS-CoV-2 proteins. Interestingly, due to the high amino acid conservation of interaction interfaces (Guharoy & Chakrabarti, 2010; Gupta et al, 2020), PPI-targeting drugs could provide unique advantages over other types of drugs such as vaccines or antivirals circumventing mutations in the pathogens' genomes that can result in immune evasive properties. Of the 26 detected high-confidence (≥95% probability) SARS-Cov-2 PPIs we detected here, 18 are known interactions according to the IMEx database (Orchard et al, 2014), while eight were not reported before (Supplementary Table  5 ).
Multi-adaptive support vector machine learning algorithm The multi adaptive supporting vector machine learning algorithm was adapted from Yang et al  (Yang et al, 2019)  and Kim et al  (Kim et al, 2021). Standardization of datasets is a common requirement for many machine learning algorithms. We observed a strong construct specific variance in the multiprotein complex reference set (Figure  EV5 ) and the binary SARS-CoV-2 mapping (Figure  EV6 ). We argued that constructs with a high variance are unlikely to form significantly more or less interactions than constructs with a low variance, but rather assumed that the observed variance is probably a technical artifact that could for example be explained by "sticky" proteins. To be able to apply a machine learning algorithm that universally applies to all constructs, we used a percentile-based scaling approach (RobustScaler, https://scikit-learn.
For 4 models of hsPRS-v2 PPIs and 6 models of hsRRS-v2 protein pairs the PAE clustering failed. For all others, the average PAE was calculated for each cluster in order to identify the interaction interface region with the closest average distance. PDBePISA was used to determine the interaction interface and solvation free energy of the AlphaFold-Multimer predicted structures (Krissinel & Henrick, 2007) using the python script PisaPy (https://github. com/hocinebib/PisaPy) for batch analysis. The PisaPy output interfacetable. xml files were processed in R using the 'read_xml' function of the 'xml2 package'. PDBePISA prediction failed for all five models of the hsRRS-v2 protein pair PSMD12+CRIPT, and for 13 hsPRS-v2 interactions and 56 hsRRS-v2 proteins at least one, but not all five models were predicted (see Figure  EV3B ).
BRET ratios were calculated as described above and normalized to solvent control wells (normalized BRET (nBRET) ratio). (A) Structures of the protein complexes analyzed in this study: LAMTOR (PDB: 6EHR), MIS12 (PDB: 5LSK), and BRISC (PDB: 6H3C). (B) Binary interaction approach to systematically map PPIs within distinct complexes.
ACKNOWLEDGEMENTS The authors would like to thank all members of the  Wanker, Vidal, Twizere and Landthaler laboratories  for helpful discussions throughout this project. We would also like to thank  Luke Lambourne  for critically reading the manuscript and providing feedback on the machine learning and AlphaFold predictions. We also thank the team of the  Protein Production & Characterization Technology Platform of the Max Delbrück Center (MDC), Berlin, Germany , for excellent technical assistance. Additional gratitude to  Sebastian Lührs  ( JURECA supercomputer, Forschungszentrum Jülich ) and  Martin Siegert  ( MaxCluster, MDC ) for technical support on cluster usage. This work was supported by the  Helmholtz.
DATA AVAILABILITY The protein interactions from this publication have been submitted to the IMEx (http://www. imexconsortium. org) consortium through IntAct (Orchard et al, 2014) and assigned the identifier IM-29174. All UniProt and RCSB-PDB accession codes, as well as the AlphaFold-Multimer predictions are provided in the Source Data. Python and R codes are available upon request.
data and selecting multiprotein complexesReference PDB structures and homologous structures for interactions in the hsPRS-v2 were obtained from interactome insider http://interactomeinsider. yulab. org/(Meyer   et al, 2018). Publicly available binary protein interaction datasets used in this study came from the original Choi et al(Choi et al, 2019)  and Yao et al (Yao et al, 2020)  publications.
The growing abundance of in vitro omics data, coupled with the necessity to reduce animal testing in the safety assessment of chemical compounds and even eliminate it in the evaluation of cosmetics, highlights the need for abundant computational methodologies. Data from omics technologies allow the exploration of a wide range of biological processes, therefore providing a better understanding of mechanisms of action (MoA) related to chemical exposure in biological systems. However, the analysis of these large datasets remains difficult due to the complexity of modulations spanning multiple biological processes. To address this, we propose a new computational workflow that combines knowledge on endogenous metabolism from a genome scale metabolic network (GSMN) and in vitro transcriptomics data with the aim of better identifying the metabolic MoA (mMoA) of chemicals.
Omics screening allows, for instance, a more holistic classification of compounds based on their global effects  [2, 3]  and it can be integrated to improve quantitative structure-activity relationships strategies  [4]. The exploration of most biological processes is covered by transcriptomics or proteomics data. Among these processes, endogenous metabolism is becoming a source of concern since chemicals have the capacity to affect metabolism at a cellular and tissue level, potentially leading to adverse effects for humans such as diabetes, obesity, or even organ dysfunction  [5] [6] [7] [8] [9]. Moreover, generating and analyzing several large-omics datasets can be expensive and methodologically challenging. Such datasets are usually of large dimensions (thousands of genes, proteins, and metabolites) that will contain information for broader insights on how an organism or a cell is globally impacted by a chemical.
One of the current key challenges in the field is to extract knowledge from these rich but complex datasets. The volumes of data generated by omics approaches and their complexity require advanced statistical and computational solutions to pinpoint patterns of interest among thousands of variables. Dimension reduction (PCA, MDS, t-SNE, etc. ) methods are widely used to describe and visualize these large multidimensional datasets, but they are not designed for functional interpretation of omics data. From that perspective, enrichment-based methods, which allow the identification of biological modules (gene sets, metabolic pathways and cellular functions) in which identified modulated variables are over-represented, have the advantage of providing an overview of the studied biological modulations  [10].
Nevertheless, these methods rely on arbitrary definitions of the pathways and functional sets, which might differ depending on the selected database, and tend to hide functional processes spanning several pathways  [11, 12]. Many studies  [13] [14] [15] [16]  aim to take advantage of published gene expression data available in databases such as DrugMatrix  [17] , Connectivity Map  [18, 19] , ToxicoDB  [20]  and Open TG-GATEs  [21]  (https://toxico. nibiohn. go. jp) to improve chemical toxicity assessment. For instance, Heusinkveld et al. [22]  implemented an approach based on the comparison of Open TG-GATEs top 50 DEG signatures ranked according to their t-statistic. They aimed at providing both a score to compare compounds and a mechanistic understanding based on enrichment-based methods.
On the other hand, Ting Li et al. [2]  trained a deep neural network model for druginduced liver injury prediction based on the LINCS L1000 dataset  [19]. In both cases, metabolism is not the primary focus of the study, and the functional interpretation relies on enrichment-based methods, with the limitations explained above. Therefore, to improve our understanding of how chemicals impact endogenous metabolism and can trigger potential adverse effects, it is necessary to develop new computational methods that would allow provision of functional metabolic information from omics data including the diversity of possible mMoAs leading to adverse outcomes. Genome-scale metabolic networks (GSMNs) are biological networks representing all the possible biochemical reactions occurring in an organism.
e. , the range of possible optimal condition-specific metabolic networks for this condition) to find a representative set of possible condition-specific metabolic networks. Hence, considering thousands of condition-specific metabolic networks is more robust and less error-prone than considering only one network in the solution space  [34]. Nevertheless, it complexifies the interpretation since a condition will be associated with thousands of potential network configurations. There is thus a need to define a new strategy to extract mechanistic information from these numerous condition-specific metabolic networks to allow the final identification of the mMoA. In this study, we propose a new semi-automated workflow designed to use transcriptomic data and GSMNs to identify and visualize mMoA. We used gene expression data from the Open TG-GATEs database to build condition-specific metabolic networks with a partial enumeration method adapted from the DEXOM  [34]  approach.
e. , cells exposed to a chemical) and transcriptomic data obtained from the Open TG-GATEs database (described in "Methods" in the section "Transcriptomic data processing"). For each studied condition, the method computes a set of condition-specific metabolic networks optimally matching gene expression data, network topology and the stoichiometry of reactions with an adapted version of DEXOM  [34]  (described in "Methods" in the section "Condition-specific modeling with partial enumeration"). DEXOM output is a dataset that gathers, for each reaction, the number of times it has been predicted as active by the algorithm. This result highlights the likelihood of each reaction of being active for a specific condition. Note that this step allows the prediction of activity for reactions, even if they are not associated with any gene (e.
Identification of differentially activated reactions (DAR) associated with exposure to eight chemicals The workflow has been applied to eight molecules (ethanol, valproic acid, indomethacin, amiodarone, allopurinol, rifampicin, sulindac, and tetracycline) selected for their known liver toxicity and the associated MoAs reported in the literature. Gene expression data for the eight molecules from the Open TG-GATEs database were used. Data generated in PHH at the highest concentration after 24h of exposure for each compound were selected along with their associated controls. Condition-specific metabolic networks were reconstructed (Fig 1  , box A ) from gene expression data for each retrieved sample (two replicates per condition). On average, 10,000 different solutions were enumerated for each sample.
Transcriptomic data processing Transcriptomic data used in this study were obtained from the Open TG-GATEs  [21]  database. For this study, we selected in vitro data generated on PHH. Raw transcriptomic data were downloaded as CEL files from https://dbarchive. biosciencedbc. jp/en/open-tggates/download. html. CEL files were read with the affy  [54]  package and the resulting dataset was normalized with the robust multiarray average method  [55]  from the limma R package. Information about the different PHH cell batches used in the different experiments was obtained from the authors (S5 Table ) and was used to correct the batch effect with Combat  [56] , available in the SVA package. We annotated the probes with the annotation database corresponding to the Affymetrix HG133U Plus 2 chips available in the hgu133plus2.
db package  [57]  and the AnnotationDbi package  [58]. Where several different probes mapped to the same gene, we selected the probe with the highest standard deviation of expression values in the dataset. Finally, to match DEXOM  [34]  requirements, we categorized transcriptomic data with Barcode  [59] [60] [61]. We used the z-scores output available from the barcode package and applied a 25/75 percentiles cutoff to obtain a list of highly and lowly expressed genes. Genes below the 25 th percentile were considered as lowly expressed genes and assigned a -1 value, while genes above the 75 th percentile were considered as highly expressed genes and assigned a +1 value. Genes between the 25 th and 75 th percentiles were not considered (i. e. , 0 value) and therefore did not have any impact on the modeling process.
DEG identification Lists of DEGs were obtained from the ToxicoDB  [20]  database, which provides pre-processed differential gene expression data from several databases, including Open TG-GATEs. A jupyter notebook querying ToxicoDB API was developed to automate the process. First, it downloads the ToxicoDB compounds json file and iterates over the list of compounds obtained from this file to query the ToxicoDB API to get analyzed data associated with each compound. The retrieved data were then filtered by selecting genes from the Open TG-GATEs human study with an absolute value of log2(FC) larger than 0. 26 and a q-value less than 0. 05, from samples subjected to a "high" dose (three doses were investigated and available for this dataset: low, middle, and high) over 24 hours. The filtered DEGs list for each compound was then stored in a.
At the end of this process, we identified a list of a priori active/inactive reactions for each sample that was then provided to DEXOM. DEXOM is a constraint-based modeling algorithm based on iMAT  [29, 30]  whose objective is to find a steady state distribution of flux that maximizes the number of reactions whose flux is consistent with transcriptomic data levels  [30]. To a representative set of solutions from the whole solution space, we adapted the partial enumeration from DEXOM and used the python implementation available in dexom-python (https://github. com/MetExplore/dexompython). First, we applied a full Reaction-enum strategy (see  [34]  for the complete description). This method iterates over all the reactions of the network, blocking each of them successively and solving the resulting mixed integer linear problem.
Because several control conditions and chemical exposure times have been used in this dataset, we computed the baseline noise for each reaction between control conditions at a specified time and a specified vehicle. Replicates were pooled for each molecule. Practically, for each reaction, we computed the R2 between all pairs of control conditions for a given vehicle at a given exposure time and calculated the median of all R2s for each reaction. Therefore, we obtained for each reaction a baseline noise estimation that is the median of all pairwise comparisons between selected control conditions. Then we filtered out DARs with an R2 between two conditions of interest (e. g. , control vs treatment) that was less than two times the noise estimate for this reaction.
Computing environment Condition-specific modeling and partial enumeration requires CPLEX v20. 10 to be installed on your system. Batch files generated by the "partial_enumeration. ipynb" notebook are designed to be launched on a SLURM computing grid but can also be launched on a standard Linux operating system. A local version of dexom-python is also required and can be cloned from the dexom-python repository (https://github. com/MetExplore/dexom-python). Finally, the network analysis workflow calling Met4J apps requires at least Java 11 to be installed on your machine.
Acknowledgements We thank  Dr. Hiroshi Yamada  for providing supplementary data regarding the cell batches for the essays from the  Open TG-GATEs database. We also thank  Dr. Pablo Rodriguez-Mier  for his expertise on how to tailor DEXOM to our needs and the discussions regarding condition-specific modeling and partial enumeration. Finally, we thank  Dr. Gladys Ouedraogo  for proofreading the manuscript and discussions throughout the project, and  Dr. Bathilde Ambroise  for the very interesting discussions we had during the very first phase of this work.
Funding This work is supported by the  Association Nationale de la Recherche et de la Technologie (ANRT)  and by the  French National Facility in Metabolomics and Fluxomics, MetaboHUB  ( 11-INBS-0010 ), launched by the  French Ministry of Research and Higher Education and the French ANR funding agency  within the  "Investissements d'Avenir" program.
Diagnostic tool design Receiver Operating Characteristic (ROC) curves were computed to test the clinical predictive power of univariate parameters (CURV, IFA, DIST, RATIO)  [17] [18] [19]. An optimized threshold value for decision was proposed for each variable  20. For multivariate datasets, we used the K-nearest neighbors (KNN) to assess predictive powers. KNN is a machine-learning procedure used to assign a new patient to a group, depending on how similar it is from its K closest neighbors in the multivariate space. The multivariate dataset was split into a subset containing 80% of all individuals, randomly chosen and used as the training dataset and a subset containing the remaining 20%, used for testing. The number of neighbors (K) required to reach a decision was determined based on the variation of the accuracy of the algorithm with K -the accuracy being the proportion of correct classi cation within the testing dataset.
Magnesium quantification assay Using the Magnesium Assay kit (Sigma-Aldrich, MAK026), the magnesium concentration was determined by a coupled enzyme assay that takes advantage of the specific requirement of glycerol kinase for Mg 2+ , resulting in a colorimetric (450 nm) product proportional to the magnesium present. This assay exhibits no detectable interference with Fe 2+ , Cu 2+ , Ni 2+ , Zn 2+ , Co 2+ , Ca 2+ , and Mn 2+. Briefly, 25µL of samples is added to 25µL of water, into duplicate in a 96-wells flat bottom plate. In parallel, standard wells containing 0 (blank), 3, 6, 9, 12 and 15 nmole/well standards of magnesium were prepared. Then, 50 µL of the Reaction Mix was added to wells containing samples and standards. The absorbance at 450 nm (A450) was measured every 5 minutes using TECAN infinite M200 PRO plate reader in kinetic mode for 30 minutes at 37°C.
Libraries were checked for quality on Bioanalyzer DNA 1000 chips (Agilent). Quantification was performed with the fluorescent-based quantitation Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific). Sequencing was performed as a Single Read run for 75 bp sequences on a NextSeq500 Illumina sequencer. The multiplexing level was 16 samples per lane. The RNA-seq analysis was performed with Sequana  (48). In particular, we used the RNA-seq pipeline (v0. 15. 1, https://github. com/sequana/rnaseq) built on top of Snakemake v6. 7. 0  (49). The pipeline trimmed reads from adapters and low quality bases using fastp software v0. 20. 1  (50) , then mapped the remaining reads to the bacteroides thetaiotaomicron genome (accession NC_004663. 1) with bowtie2 v2. 4. 2  (51). Mapped reads were then quantified using featureCounts  (52) , and differential analysis performed with DESeq2 v1.
ACKNOWLEDGEMENTS We thank  Nathalie Béchon ,  Sol Vendrell-Fernandez ,  Christiane Forestier ,  Muriel Masi ,  Christophe Beloin  and  Yutaka Yoshii  for critical reading of the manuscript. This work was supported by  Institut Pasteur  and grants by the  ANR DifBiolRel  (grant n°  ANR 20CE15002201 ), the  French government 's  Investissement d'Avenir Program ,  Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases"  (grant  n°ANR-10-LABX-62-IBEID ), the  Fondation pour la Recherche Médicale  (grant no. DEQ20180339185 ). A. A. Lopes was supported by a " Poste d'accueil   AP-HP "  2019-2022. The RNA sequencing and analysis was performed by the  Biomics Platform , C2RT,  Institut Pasteur, Paris, France , supported by  France Génomique  ( ANR-10-INBS-09-09 ) and  IBISA.
Data analyzes: One data set from one participant was corrupted because of a technical issue and was removed from further analyzes. All remaining data were included in analyzes performed with Matlab R2018b (The MathWorks, Natick, MA, USA). The raw pupil data, down-sampled at 60 Hz, were first corrected for blinks and recordings artifacts. Blinks were detected as zeros in the pupillary traces. The corresponding blink data were replaced by a smoothed linear interpolation, using a pre-and post-blink offset of 4 samples (66. 6 msec. ). As the pupil is unlikely to change rapidly, we then performed a second pass to detect "artifactual transients" for which the pupillary signal was higher than a threshold, calculated using an ad hoc formula combining velocity and acceleration values, set once for the whole dataset.
Acknowledgments: We are very grateful to all the participants who agreed to participate in the different studies presented here. Thanks to  Richard Legras  who accepted to host the present study, and to the students of the department of  Optometry at Orsay University  who ran the tests. Thanks to  Raphael Barbet  who performed data acquisition in some control experiments. We also wish to warmly thank  Dr. Frederic Matonti ,  Laure Trinquet ,  Frederic Chavane ,  Dr. Aurélien Stélandre ,  Prof. Jean-François Rouland ,  Dr. Catherine Vignal-Clermont ,  Dr. Cédric Lamirel , and  Prof. Antoine Labbé  from whom the author learned a wealth of information regarding ophthalmology. Thanks to  Benoit Cottereau  and  Mark Wexler  for their relevant advices and suggestions, and to colleagues from the lab for their constructive comments.