dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation of predicted modules is challenging because there is no ground truth of 'correct' modules in molecular networks. Here, we introduce a framework to empirically assess modules based on their association with complex traits and diseases using genome-wide association studies (GWAS) data (Fig. 1c ). Since GWAS are based on data completely different from those used to construct the networks, they can provide independent support for biologically relevant modules. To cover diverse molecular processes, we have compiled a large collection of 180 GWAS datasets (Supplementary Table  1 ). Predicted modules were scored on each GWAS using the Pascal tool  23  , which aggregates traitassociation P values of single nucleotide polymorphisms (SNPs) at the level of genes and modules. Modules that scored significantly for at least one GWAS trait were called trait-associated.
Due to linkage disequilibrium (LD), many genes that show association to a trait may not causally influence it. A key question is whether our trait modules, and the corresponding genes, are correctly predicted as being biologically or therapeutically relevant for that trait or disease. We thus sought to evaluate trait modules using additional independent datasets, including ExomeChip data, monogenic disease genes, functional annotations and known therapeutic targets. We first consider a module from the consensus analysis that shows association to height-a classic polygenic trait-as an example (Fig. 5a ). Forty percent of genes in this module either comprise coding variants associated to height in an independent ExomeChip study  32  or are known to be implicated in monogenic skeletal growth disorders, supporting their causal role in the phenotype (Fig.
5b ). Gene Ontology (GO) annotations further show that this module consists of two submodules comprising extracellular matrix proteins responsible for, respectively, collagen fibril and elastic fiber formation-pathways that are essential for growth (Fig. 5b ). Indeed, mutations of homologous genes in mouse lead to abnormal elastic fiber morphology (Supplementary Fig. 9 ). Some of the genes supported by these additional datasets did not show signal in the GWAS used to discover the module. For example, the module gene BMP1 (Bone Morphogenic Protein 1) The module includes genes that are genome-wide significant (magenta and pink) as well as genes that do not reach the genome-wide significance threshold, but are predicted to be involved in height due to their module membership (blue and gray). b, Member genes of the height-associated module are supported by independent datasets: 24% of module genes are implicated in monogenic skeletal growth disorders (red squares, enrichment P = 7.
To evaluate more generally whether trait-associated modules correspond to generic or disease-specific pathways, we systematically tested modules for functional enrichment of GO annotations, mouse mutant phenotypes and pathway databases. We further selected the most representative annotations for each module using a regression framework  33  (Methods). We find that the majority of trait modules reflect core disease-specific pathways. For example, in the STRING protein-protein interaction network only 33% of trait modules from the consensus analysis have generic functions; the remaining 66% of trait modules correspond to core disease-specific pathways, some of which are therapeutic targets (Supplementary Fig. 12  and Supplementary Table  4 ). Examples include a module associated with rheumatoid arthritis that comprises the B7:CD28 costimulatory pathway required for T cell activation, which is blocked by an approved drug (Fig.
6a ); a module associated with inflammatory bowel disease corresponding to cytokine signaling pathways mediated by Janus kinases (JAKs), which are therapeutic targets (Fig. 6b ) and a module associated with myocardial infarction that includes the NO/cGMP signaling cascade, which plays a key role in cardiovascular pathophysiology and therapeutics (Fig. 6c ). We further applied our pipeline to a GWAS on IgA nephropathy (IgAN) obtained after the challenge, an autoimmune disorder with poorly understood etiology  34. We find two IgAN-associated modules, which prioritize novel candidate genes involved in NF-kB signaling, complement and coagulation cascades, demonstrating how our challenge resources can be used for network-based analysis of new GWAS datasets (Supplementary Fig. 13 ).
Discussion As large-scale network data become pervasive in many fields, robust tools for detection of network communities are of critical impor-tance. With this challenge we have conducted an impartial and interpretable assessment of module identification methods on biological networks, providing much-needed guidance for users. While it is important to keep in mind that the exact ranking of methods is specific to the task and datasets considered, the resulting collection of top-performing module identification tools and methodological insights will be broadly useful for modular analysis of complex networks in biology and other domains. Kernel clustering, modularity optimization, random-walk-based and local methods were all represented among the top performers, suggesting that no single type of approach is inherently superior.
Node colors correspond to Pascal gene scores in the respective GWAS (Methods). For the two inflammatory disorders (a,b), red squares indicate genes causing monogenic immunodeficiency disorders (enrichment P values of 4. 1 × 10 -8 and 1. 2 × 10 -6 , respectively (one-sided Fisher's exact test)). a, Module associated with rheumatoid arthritis (n = 25 genes, FDR-corrected Pascal P = 0. 04) that is involved in T cell activation. A costimulatory pathway is highlighted green, T cell response is regulated by activating (CD28) and inhibitory (CTLA4) surface receptors, which bind B7 family ligands (CD80 and CD86) expressed on the surface of activated antigen-presenting cells. The therapeutic agent CTLA4-Ig binds and blocks B7 ligands, thus inhibiting T cell response. b, Cytokine signaling module associated with inflammatory bowel disease (n = 42 genes, FDR-corrected Pascal P = 0.
0006). The module includes the four known Janus kinases (JAK1-3 and TYK2, highlighted green), which are engaged by cytokine receptors to mediate activation of specific transcription factors (STATs). Inhibitors of JAK-STAT signaling are being tested in clinical trials for both ulcerative colitis and Crohn's disease  44. c, Module associated with myocardial infarction (n = 36 genes, FDR-corrected Pascal P = 0. 0001) comprising two main components of the NO/cGMP signaling pathway (endothelial nitric oxide synthases (NOS1-3) and soluble guanylate cyclases (GUCY1A2, GUCY1A3 and GUCY1B3), highlighted green), a key therapeutic target for cardiovascular disease  45.
Methods Network compendium. A collection of six gene and protein networks for human were provided by different groups for this challenge. The two protein-protein interaction and signaling networks are custom or new versions of existing interaction databases that were not publicly available at the time of the challenge. The remaining networks were yet unpublished at the time of the challenge. This was important to prevent participants from deanonymizing challenge networks by aligning them to the original networks. Networks were released for the challenge in anonymized form. Anonymization consisted in replacing the gene symbols with randomly assigned ID numbers. In Sub-challenge 1, each network was anonymized individually; that is, node k of network A and node k of network B are generally not the same genes.
In Subchallenge 2, all networks were anonymized using the same mapping; that is, node k of network A and node k of network B are the same gene. All networks are undirected and weighted, except for the signaling network, which is directed and weighted. Below we briefly summarize each of the six networks. Detailed descriptions of networks 4, 5 and 6 are available on GeNets  20  , a web platform for network-based analysis of genetic data (http://apps. broadinstitute. org/genets). The first network was obtained from STRING, a database of known and predicted protein-protein interactions  14. STRING includes aggregated interactions from primary databases as well as computationally predicted associations. Both physical protein interactions (direct) and functional associations (indirect) are included.
The challenge network corresponds to the human protein-protein interactions of STRING v. 10. 0, where interactions derived from text mining were removed. Edge weights correspond to the STRING association score after removing evidence from text mining. The second network is the InWeb protein-protein interaction network  15. InWeb aggregates physical protein-protein interactions from primary databases and the literature. The challenge network corresponds to InWeb v. 3. Edge weights correspond to a confidence score that integrates the evidence of the interaction from different sources. The third network is the OmniPath signaling network  16. OmniPath integrates literature-curated human signaling pathways from 27 different sources, of which 20 provide causal interaction and seven deliver undirected interactions.
The matrix was thresholded to include the top 1 million strongest positive correlations resulting in an undirected, weighted network. The edge weights correspond to the correlation coefficients. The fifth network is a functional gene network derived from the Project Achilles dataset v. 2. 4. 3 (ref. 18 ). Project Achilles performed genome-scale lossof-function screens in 216 cancer cell lines using massively parallel pooled short-hairpin RNA screens. Cell lines were transduced with a library of 54,000 shRNAs, each targeting one of 11,000 genes for RNA interference knockdown (~5 shRNAs per gene). The proliferation effect of each shRNA in a given cell line could be assessed using Next Generation Sequencing. From these data, the dependency of a cell line on each gene (the gene essentiality) was estimated using the ATARiS method.
The number of modules per network was not fixed: teams could submit any number of modules for a given network (the maximum number was limited due to the fact that modules had to be non-overlapping). In Sub-challenge 1, teams were required to submit a separate set of modules for each of the six networks. In Subchallenge 2, teams were required to submit a single set of modules by integrating information across multiple networks (it was permitted to use only a subset of the six networks). The challenge consisted of a leaderboard phase and the final evaluation. The leaderboard phase was organized in four rounds, where participants could make repeated submissions and see their score for each network, along with the scores of other teams, on a real-time leaderboard. Due to the high computational cost of scoring the module predictions on a large number of GWAS datasets, a limit for the number of submissions per team was set in each round.
The submission format and rules are described in detail on the challenge website (https://www. synapse. org/modulechallenge). Gene and module scoring using Pascal. We have developed a framework to empirically assess module identification methods on molecular networks using GWAS data. Since we are employing a large collection of 180 GWAS datasets ranging over diverse disease-related human phenotypes, this approach covers a broad spectrum of molecular processes. In contrast to evaluation of module enrichment using existing gene and pathway annotations, where it is sometimes difficult to ascertain that annotations were not derived from similar data types as the networks (for example, gene expression, protein-protein interactions or homology), the GWAS-based approach provides an orthogonal means to assess disease-relevant modules.
SNP trait-association P values from a given GWAS were integrated across genes and modules using the Pascal (pathway scoring algorithm) tool  23. Briefly, Pascal combines analytical and numerical solutions to efficiently compute gene and module scores from SNP P values, while properly correcting for LD correlation structure prevalent in GWAS data. To this end, LD information from a reference population is used (here, the European population of the 1,000 Genomes Project was employed as we only included GWASs with predominantly European cohorts). For gene scores we used the sum of chi-squared statistics of all SNPs within a window extending 50 kb up and downstream from the gene of interest. Since proximal SNPs are often in LD, under the null hypothesis this sum is not distributed like a sum of chi-squares of independent random variables.
Yet, a change of basis to orthogonal 'eigen-SNPs' , which diagonalize the genotypic correlation (LD) matrix, recovers independence with the effect that the sum of independent chi-squares is weighted (with the eigenvalues as weights)  23. The fast gene scoring is critical as it allows module genes that are in LD, and can thus not be treated independently, to be dynamically rescored. This amounts to fusing the genes of a given module that are in LD and computing a new score that takes the full LD structure of the corresponding locus into account. Pascal tests modules for enrichment in high-scoring (potentially fused) genes using a modified Fisher method, which avoids any P value cutoffs inherent to standard binary enrichment tests. The general approach can be summarized in three steps: (1) gene score P values of all genes in the background set (here, all genes in a given network) are transformed so that they follow a target distribution respecting their ranking; (2) a test statistic for a given module is computed by summing the transformed scores of module genes (and fusion-genes) and (3) it is evaluated whether the observed test statistic is higher than expected, that is, the module is enriched for trait-associated genes.
Specifically, here we employed the 'chi-squared method' implemented in Pascal  23  , which transforms gene scores such that they follow a χ Since gene scores are first rank-transformed, this is a 'competitive' enrichment test, which evaluates whether the module genes tend to have lower trait-association P values than the other genes that are part of the given network. Note that specifying the correct background set is critical for competitive enrichment tests, which here amounts to all genes that are part of the given network (that is, not all genes in the genome), as shown in Supplementary Fig. 4. Last, the resulting nominal module P values were adjusted to control the FDR via the Benjamini-Hochberg procedure. Scoring metric. In Sub-challenge 1, the score for a given network was defined as the number of modules with significant Pascal P values at a given FDR cutoff in at least one GWAS (called trait-associated modules, see previous section).
Leaderboard and holdout GWAS datasets. We compiled a collection of 180 GWAS datasets (Supplementary Table  1 ), including all GWASs for which we could access genome-wide summary statistics (SNP P values). We deliberately included both high-and low-powered studies to evaluate whether disease-associated modules could be detected in datasets of varying signal strength. We manually assigned each GWAS dataset to either the leaderboard set used in the leaderboard round or the holdout set used for the final scoring. The assignment was made such that GWASs of closely related traits (for example, height, male height and female height) were either all in the leaderboard set or all in the holdout set, thus avoiding a situation where two very similar GWASs would be found both in the leaderboard and holdout set.
Acknowledgements The challenge was hosted on Sage Bionetwork's Synapse platform (https://synapse. org/). The computations were performed at the  Vital-IT  (http://www. vital-it. ch)Center for high-performance computing of the  SIB Swiss Institute of Bioinformatics. This work was supported by the  Swiss National Science Foundation  (grant no. FN 310030_152724/1  to S. B. and grant no. FN 31003A-169929  to Z. K. ),  SystemsX. ch (grant no. SysGenetiX to S. B. and grant no. AgingX to Z. K. ), the  Swiss Institute of Bioinformatics  (Z. K. and S. B. ), the  US National Science Foundation  (grant no. DMS-1812503  to L. C. and X. H. ) and the  National Institutes of Health  (grant no. R01 HD076140  to D. K. S. ).
Code availability Code is available on GitHub for: user-friendly, dockerized versions of the top three methods (https://github. com/BergmannLab/MONET); the latest Pascal version (https://www2. unil. ch/cbg/index. php?title=Pascal, https://github. com/dlampart/ Pascal); the regression-based gene-set enrichment analysis (https://github. com/ TaoDFang/GeneModuleAnnotation) and, in addition, the scoring scripts, a snapshot of the Pascal version used for the challenge, and module identification method descriptions and code provided by teams are available on the challenge website (https://synapse. org/modulechallenge).
Participation in other trials Participants included in the SafeBoosC III trial can participate in any other study or intervention on the condition that: it does not allow clinical staff access to cerebral oximetry in the control group from inclusion in SafeBoosC III to the end of the intervention period 72 h after birth; and does not exclude a treatment that would be clearly indicated by the SafeBoosC III evidence-based treatment guideline during the intervention period. All partners are encouraged to design ancillary studies and draw on data collected by SafeBoosC III, if not compromising the blinding of assessors or the equipoise of the trial. Ancillary studies must seek approval by the SafeBoosC Steering Committee.
Participant discontinuation and withdrawal A participant's parents are free to withdraw them from the SafeBoosC III trial at any time, and this will not have any consequences for the infant's further treatment. Reasons for discontinuation, if provided by the parents, will be documented. When possible, the parents will be asked if they will allow their child's data to be used in the analysis. The attending clinician can withdraw the participant from the trial at any time in case there are safety concerns. Reasons for withdrawal will be documented. There are no pre-specified criteria for discontinuation of participants from the trial. Discontinuation of participants from the trial will not result in replacement with new participants.
Blinding Due to the nature of the experimental intervention, it is not possible to blind the clinical staff, the infant, or the parents to study group allocation. Outcome assessment of mortality will not be blinded but the mortality data will be checked by Good Clinical Practice via source data verification in all patients. The diagnosis and classification of brain injury along with the entry of these data into the patient report form will be conducted by an assessor blinded to study group allocation. Data entry procedures will depend on local factors and will be agreed on between the principal investigator at each NICU and the coordinating investigator. The data managers, statisticians, and those drawing conclusions will be blinded to study group allocation. Details on this is described in a report on the statistical analysis plan  [23].
Explanatory variables To allow comparisons between intervention groups, additional baseline clinical data will be obtained, including birth weight, gestational age, mechanical ventilation, and use of cardiovascular support. Data will be drawn from clinical records at 72 h of age and 36 weeks postmenstrual age, the same time as the primary and exploratory outcomes are assessed and documented. The majority of these selected variables are usually reported to neonatal network databases such as the Vermont Oxford Network  [26].
Outcomes Primary and exploratory outcomes will be assessed at 36 weeks postmenstrual age as documented in the infants' clinical files. If an infant has been discharged to a step-down unit, data will be sought from that unit, and if this is not possible, data will be used until the date of discharge to the step-down unit. In case the last entry in an infant's clinical file is prior to 36 + 0 weeks postmenstrual age, for example due to discharge home, the date of discharge will be reported in the online patient report form. The primary outcome is a composite of either death or severe brain injury detected on any one of a series of cranial ultrasound scans that are routinely performed in extremely premature infants. Severe brain injury is defined as grade III or IV intraventricular haemorrhage (IVH), cystic periventricular leukomalacia (cPVL), cerebellar haemorrhage, post-haemorrhagic ventricular dilatation, or cerebral atrophy.
Safety Predefined serious adverse reactions (SAR) will be reported at 72 h after birth and serious adverse events (SAE) will be reported at 36 weeks postmenstrual age. Expedited reporting will not be used. An independent data monitoring and safety committee is established to monitor mortality, neonatal morbidity, and SARs with 'certain' or 'probably/likely' relationships with the cerebral NIRS oximeter and/or the application of the evidence-based treatment guideline or any of its interventions. They include two neonatologists and a biostatistician. The charter for the data monitoring and safety committee has been written prior to the enrolment of trial participants. The trial will not be stopped early because of futility, and Lan-DeMets sequential monitoring boundaries will be used at each interim analysis to assess if thresholds for statistical significance of benefits or harms have been crossed  [29].
Only one interim analysis is planned, after one-third of trial participants have been randomised. Additional analyses will be decided by the data monitoring and safety committee members  [23]. Based on primarily safety considerations, the data monitoring and safety committee will make recommendations to the steering group to continue, change, hold, or terminate the trial. The recommendations will be guided by the statistical monitoring guidelines, which is defined in the data monitoring and safety committee charter (available from www. safeboosc. eu). The preterm population is at high risk for SAEs and most adverse events may be of a serious nature with or without relevance to the SafeBoosC III trial intervention. Both groups of the trial are expected to have a high proportion of SAEs. It is therefore neither feasible nor meaningful to record and report all adverse events.
Data management All participants' data are protected in accordance with the Danish Act on the processing of personal data and the Danish Health Act. The Copenhagen Trial Unit will provide central, web-based data entry through an online patient report form, in the open-source clinical trial software OpenClinica®. This will handle the inclusion procedure, the documentation of the stratification and randomisation process, the SARs, and the relevant clinical data from enrolled subjects, including primary and exploratory outcomes and explanatory variables. The data will be entered into the online patient report form directly by the medical staff. Forms for randomisation/ inclusion, end-of-monitoring at 72 h of age, and the 36week follow-up will be created. Data will be stored in accordance with guidelines issued by the Danish Data Protection Agency, from whom approval of the trial will be sought.