dataesr/scientific-paragraphs-categorization huggingface dataset:
Results To the best of our knowledge, MOGAMUN is the first algorithm that detects active modules from networks. However, several methods exist to detect one  [4, 10, 11] , or several  [3, 5, 6, 9, 12]  active modules in monoplex networks -aka single networks. We here compare MOGAMUN with three state-of-the-art approaches to detect active modules in monoplex networks (section 3. 1). We then applied our algorithm to study Facio-Scapulo-Humeral muscular Dystrophy type 1 (FSHD1), using a multiplex network (section 3. 2).
MOGAMUN against state-of-the-art active module identification methods We ran jActiveModules, COSINE, PinnacleZ and MOGAMUN 30 times (see Materials and methods). The execution times per run of each algorithm, in a desk computer with Intel processor i7 at 3. 60GHz and 32GB of RAM, were approximately 30 min, 8 hours, 30 min, and 12 hours for jActiveModules, COSINE, PinnacleZ and MOGAMUN, respectively. As a first experiment, we used the PPI_1 network (Table  2 ) and the Sim_normal dataset (Table  3 ) (see Materials and methods). The goal is to retrieve the active module, which is a single subnetwork composed of 20 nodes (i. e. , the foreground genes (FG)). The four methods retrieved the 20 nodes of the FG. PinnacleZ retrieved 13 231 subnetworks in total, corresponding to 494 subnetworks with at least one different node.
These 494 subnetworks have an average size of 6 nodes and 3% average Jaccard similarity between them. COSINE and jActive-Modules retrieved 30 subnetworks each, one per run. The 30 subnetworks found by COSINE all have at least one different node, an average size of 640 nodes and 5% average Jaccard similarity. Finally, 29 out of the 30 subnetworks retrieved by jActiveModules have at least one different node, an average size of 6 952 nodes and 76% average Jaccard similarity. MOGAMUN retrieved 6 modules with at least one different node, an average size of 17 nodes and 13% average Jaccard similarity. We calculated the F 1 score (Materials and Methods) of the union of all the active modules retrieved by each method on the 30 runs. The F 1 score determines how good the methods are to retrieve the foreground genes (FG) while avoiding picking background genes (BG).
The F 1 score for jActiveModules, COSINE and PinnacleZ is close to zero (F jActiveModules  PinnacleZ indeed restricts the search locally around the seed neighborhood by considering only the subset of nodes that are (at most) two steps away from the seed. In such small modules, the two objectives are expected to reach their maximum values. For example, a subnetwork composed only of two significant nodes with scores = 1 linked by an interaction will have a maximal average nodes score, as well as a maximal subnetwork density because it is a complete graph. In order to consider this, we filtered all the modules obtained by the four methods to keep only the subnetworks with at least 15 nodes (Fig  3B ). This removed all the active modules obtained by PinnacleZ, and revealed that MOGAMUN succeeded to find the best results, in terms of the two objectives.
MOGAMUN is the only approach allowing setting a minimum allowed size from the four methods implemented here. It is to note that, if we also remove the subnetworks with more than 50 nodes (the maximum size we set in MOGAMUN and PinnacleZ, the two methods allowing this setting), we would also discard all the results from COSINE and jActiveModules, which obtain subnetworks with hundreds or even thousands of nodes. In a second test, we used the PPI_2 network (Table  2 ) and the Samp_TCGA dataset (Table  3 ). The goal is also to retrieve a single active module, which is a subnetwork composed of 20 nodes (i. e. , the foreground genes (FG), see Materials and methods). jActiveModules retrieved the highest number of FG genes  (19/20)  The filtering of the modules having more than 15 nodes in this comparison also removed all the results obtained by PinnacleZ, as well as two high-scoring subnetworks from jActive-Modules (Fig  3D ).
After filtering on module size, MOGAMUN led to the best results, although jActiveModules succeeded in finding a module with an average nodes score similar to one of MOGA-MUN. However, MOGAMUN found overall denser subnetworks. This is expected as MOGAMUN is the only approach also optimizing subnetwork densities. If we also remove the subnetworks with more than 50 nodes, we would again discard all the results from COSINE and jActiveModules, with the exception of a single subnetwork, found by jActiveModules. In summary, MOGAMUN clearly identifies the best modules in terms of the multiple objective setting. Moreover, the retrieved modules have reasonable and tunable sizes.
Overall, the biological processes leading to the muscle defects remain currently unclear. We aim here to apply MOGAMUN in order to reveal biological processes that would not have been exposed by previous analyses, and further define biomarkers associated with the muscle phenotype of patients. We applied MOGAMUN using a multiplex network composed of three layers of biological interactions and FSHD1 RNA-sequencing expression datasets obtained from different types of cells  [34] [35] [36]  (Materials and Methods). More precisely, the first FSHD1 RNA-seq datasets were obtained from biopsies, myoblasts and myotubes differentiated from those myoblasts  [34]. The two other datasets were obtained from immortalized myoblasts  [35]  and myotubes differentiated from those myoblasts  [36]  (Materials and Methods).
We independently ran MOGAMUN 30 times. We first considered the results obtained from  [34]  datasets, and analyzed the active modules identified by MOGAMUN in biopsies  (. We also analyzed these three expression datasets with the other active module identification methods (jActiveModules, PinnacleZ, COSINE). Overall, these different active module identification approaches identified either extremely small or extremely large modules (see Supplementary Section 5 in S1 File for details on the results obtained by all the methods). MOGAMUN is the approach providing the best trade-off, as it allows retrieving modules that can be easily used for further biological interpretation. In Yao's dataset, myoblasts, all the 10 active modules obtained by MOGAMUN contain at least one of the two significantly down-regulated genes LRRTM4 and GFRA1 (Supplementary Fig S10 in S1 File).
LRRTM4 is required for presynaptic differentiation and GFRA1 belongs to the GDNF family receptor, also involved in the control of neuron survival and differentiation. The function of these two factors in muscle cells is, to our knowledge, not described. However, it is interesting to note that they belong to active modules containing proteins implicated in ubiquitination, intracellular signaling and DNA replication machinery, as FSHD1 cells display increased apoptosis and reduced proliferation. Using expression data obtained from myotubes differentiated from these myoblasts (Yao's dataset, myotubes), MOGAMUN identified 23 active modules, among which many contain a subset of highly over-expressed nodes (Supplementary Fig S11 in S1 File). These nodes associated to a high fold-change are DUX4 target genes, as defined in Geng et al.
Nodes with bold black border correspond to genes significantly differentially expressed (FDR <0. 05 and absolute log 2 fold-change >1). Blue nodes correspond to genes with no associated transcriptomics data. The color of the edges represents the layer of the multiplex network, where blue, orange, and yellow correspond to PPI, Pathways, and Co-expression, respectively. The active modules are extracted from the sets of active modules obtained from (A) Yao's dataset, myotubes  [34] , (B) Yao's dataset, biopsies  [34] , (C) Banerji's 2017 dataset  [35] , and (D) Banerji's 2019 dataset  [36]. https://doi. org/10. 1371/journal. pcbi. 1009263. g004 In biopsies, Yao et al. also detected some DUX4 targets genes at a low level  [34]. However, we do not identify DUX4 target genes in active modules obtained by MOGAMUN from biopsy expression data.
Notably, in Yao's dataset, biopsies, we identified an interesting active module containing ACTC1 (encoding the Alpha Actin Cardiac Muscle 1) (Fig  4B ). ACTC1 is mainly expressed in developing skeletal muscle, but its expression is also reactivated in diseased mature skeletal muscle, possibly as a sign of regeneration. In this module, we also noticed the presence of the over-expressed gene ABCC8, encoding a modulator of ATP-sensitive potassium channel and insulin release, and involved in the control of contractility and protection of the tissue against calcium overload and fiber damage. An intriguing observation is also the presence of OGDHL, encoding the 2-oxoglutarate deshydrogenase complex component E1-like, which localizes to mitochondria and degrades glucose and glutamate. OGDHL is significantly down-regulated in diseased biopsies.
Overall, this active module links a potential reduced mitochondrial activity, frequently described for FSHD1, to an increased expression of factor involved in the calcium release when cellular energetics is compromised, and possible dysfunction of the contractile apparatus associated with over-expression of ACTC1, which might reveal an increased muscle regeneration or immaturity of the contractile apparatus. In  Banerji et al. 2017 , the authors highlighted the repression of PAX7 target genes as a hallmark of FSHD1 skeletal muscle  [35]. This signature, associated with the activation of the hypoxia pathway, was considered as more robust than the DUX4 signature, which remains variable between studies  [34, 44, 46]. We identified 23 active modules with MOGAMUN using Banerji's 2017 dataset  (Supplementary Fig S12 in S1 File).
The nodes belonging to these modules, as observed for instance in Fig 4C, reveal MAPK-dependent decrease in cell signaling pathways, response to oxydative stress and reduced cell proliferation, as often reported for FSHD1 cells in culture. We finally applied MOGAMUN to Banerji's 2019 dataset, which corresponds to RNA-seq data from myotubes derived from immortalized myoblasts  [36]. This RNA-seq study was designed to consider the temporal dimension of gene expression. Genes are classified into 6 categories divided in 3 different groups: up or down regulated in FSHD1; up or down regulated during myogenesis and up or down regulated during FSHD1 myogenesis. One of the main message of this work relates to the suppression of PGC1α (encoded by the PPARGC1A gene) in FSHD1 myotubes as a cause of hypotrophy in FSHD1 myotubes  [36].
We applied MOGAMUN only to RNA-seq data obtained from the last time point of the myoblast to myotubes differentiation kinetics (i. e. fully differentiated post-mitotic myotubes) (Materials and Methods). We identified 17 active modules (Supplementary Fig S13  in S1 File ). An interesting module revealed, among other, connections between PPARGC1A and CPT1C (Fig  4D ). PPARGC1, down-regulated in FSHD1, is involved in regulating the activities of cAMP response element binding protein (CREB) and nuclear respiratory factors (NRFs). CPT1C, upregulated in FSHD, is involved in muscle glucose uptake. We also observed in the module the presence of MAPK10, required for protection against apoptosis. It is to note that MAPK10 is also identified in a module from FSHD1 immortalized myoblasts Fig 4C ), overall highlighting the existence of connections between specific signalling pathways and chromatin-associated factors previously identified as implicated in the disease (YY1, EP300, CREBBP)  [46, 47].
Discussion We here designed, compared and applied MOGAMUN, a multi-objective genetic algorithm that is able to detect active modules in multiplex networks. Multiplex biological networks are composed of different layers of physical and functional interactions; each layer has its own meaning, topology and noise. The protein-protein interaction layer, for example, is sparse, but composed of physical binary interactions extracted from curated databases. On the other hand, the co-expression network is very dense, but prone to indirect and spurious interactions. However, altogether, the different layers can provide complementary functional information  [16, 17]. We compared MOGAMUN to three different methods, representative of the main algorithms dedicated to the identification of active modules: greedy searches (PinnacleZ), simulated annealing (jActiveModules), and mono-objective genetic algorithm (COSINE).
As, to our knowledge, no existing method is able to leverage multiplex networks as inputs, we designed benchmark for comparison that is based on single networks. In order to have a fair comparison between MOGAMUN and the three other methods, we used the parameters recommended by the authors, except when parameter values could be tuned to match the benchmark scenario or the parameters selected for MOGAMUN (Table  1 ). In particular, we set the number of subnetworks to be retrieved by jActiveModules to one in the benchmark because there was a single active module. In addition, we set the maximum size per subnetwork to 50 nodes in PinnacleZ. Finally, in COSINE, we set the lambda parameter to 0. 5, to have a tradeoff between the weights of the nodes and edges. Both the weights of the nodes and edges in COSINE are calculated from the expression data (section 2.
4. 3), and they cannot be manipulated by the user. Importantly, none of the four methods compared in this manuscript can handle weighted networks. In the benchmark analyses, we observed that jActiveModules and COSINE tend to retrieve very large subnetworks (up to hundreds or even thousands of nodes), whereas PinnacleZ tends to retrieve very small ones (mostly composed of 2 or 3 nodes). Although jActiveModules, COSINE and PinnacleZ were able to retrieve most or all of the simulated foreground genes, their F 1 scores were lower than those of MOGAMUN because they selected more background genes (i. e. , false positives). Importantly, only PinnacleZ and MOGAMUN have a user-defined threshold to limit the maximum size of the modules, and MOGAMUN is the only method having a user-defined threshold to limit the minimum size of the modules.
In addition to the benchmark with artificial expression data, we also applied the four methods to the three RNA-Seq datasets of FSHD1 from  [34]. MOGAMUN is the only method that can take as input more than one network. Hence, in order to apply all the approaches, we aggregated the three network layers of the multiplex biological network (Table  4 ). The behaviour of jActiveModules, COSINE and PinnacleZ is similar in this real-case experiment as in the benchmark analyses (see Supplementary Section 5 in S1 File for detailed results). jActive-Modules and COSINE tend to retrieve large subnetworks, and PinnacleZ finds only very small ones (Supplementary Figs S17-S25 in S1 File). We further observed that, as expected, the simple density (Eq 4) is higher for MOGAMUN active modules obtained from the aggregated networks, whereas the normalized density (Eq 3) is higher for MOGAMUN active modules obtained from the multiplex network (Supplementary Figs S17-S25 in S1 File).
In addition, since the aggregated network is denser than any of the individual network layers, the chances of connecting high-scoring nodes is also higher. In the future, a benchmark based on biological proxies, using for instance Gene Ontology annotation enrichments  [48] , as recently proposed in the DOMINO approach  [49] , could be used to compare the active modules obtained from aggregated versus multiplex networks. We demonstrated the performance of MOGAMUN in retrieving modules both densely connected and containing top-scoring nodes, i. e. , nodes associated with a high deregulation. The two objective functions of MOGAMUN overall allow using an aggregated node score to optimize the differential expression of the subnetworks while balancing their size. However, MOGAMUN tends to find subnetworks of the minimum size allowed (or very close to it).
This is expected, given the sparse nature of biological networks. This bias towards minimum size active modules could lead to having a large active module cut into several smaller subnetworks, which are nonetheless easier to visualize and interpret. Overall, the MinS parameter has to be chosen carefully, depending on the user's needs. We implemented in addition a post-processing step to merge overlapping subnetworks, according to a user-defined threshold. It is to note that, given the small-world property of biological networks, if this threshold is too low, the result might be a single (large) active module. We therefore recommend trying different thresholds values. MOGAMUN running time is, similarly to the other genetic algorithm COSINE, one order of magnitude slower than jActiveModules and PinnacleZ in its current implementation.
This running time could be by implementing the most computationally demanding tasks (e. g. , crossover) in lower programming languages, like C or C++, or using surrogate-assisted multi-objective evolutionary algorithms. The extensive analysis of the different FSHD1 datasets highlighted the reduced proliferation and increased apoptosis of cells from FSHD1 patients and led to the identification of novel genes in these different pathways by linking cell defects to factors involved in muscle function. It further revealed consistencies in biological processes identified by different teams in their respective models but also some putative discrepancies in the interpretation of disease-associated biological processes depending on the type of samples used (biopsies of muscle unaffected in the disease, immortalized or transduced proliferative myoblasts or post-mitotic myotubes).
Overall, this also reveals that MOGAMUN can be applied to identify disease-associated biological processes in rare diseases for which the number of samples is limited, and also to compare the processes identified in different datasets. We applied here MOGAMUN to identify active module from the integration of RNA-seq expression data into multiplex networks. However, it is to note that any type of molecular profile associated to p-values can be integrated on the networks, such as p-values obtained from a GWAS, from phenotypic hit screening, or from proteomics profiling. 1-17. 2. Supplementary Tables. Table  S1 : Samples from Yao's datasets. Downloaded from https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE56787. Table  S2 : Samples from Banerji's 2017 dataset. Downloaded from https://www.
ncbi. nlm. nih. gov/geo/query/acc. cgi? acc=GSE102812. Table  S3 : Samples from Banerji's 2019 dataset. Downloaded from https:// www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE123468. 3. Non-dominated Sorting Genetic Algorithm II (NSGA-II). Algorithm S1: Fast non-dominated sorting. The density is computed either on the aggregated network, corresponding to the union of the three biological networks used in this study, or using the multiplex-normalized density, proposed in the main manuscript. Table  S4 : Number of genes and number and percentage of differentially expressed genes (DEGs) retrieved in the active modules by the different methods in 30 runs. Table  S5 : Number of genes and number and percentage of differentially expressed genes (DEGs) retrieved in the active modules by the different methods in 30 runs.
The sizes of the subnetworks are represented on a log scale. The density is computed either on the aggregated network, corresponding to the union of the three biological networks used in this study, or using the multiplex-normalized density, proposed in the main manuscript. Fig S19: Size, average nodes score and density of the subnetworks obtained by the different methods using Yao's dataset, biopsies, selecting only the subnetworks containing at least 15 nodes. The sizes of the subnetworks are represented on a log scale. The density is computed either on the aggregated network, corresponding to the union of the three biological networks used in this study, or using the multiplex-normalized density, proposed in the main manuscript. Fig S20: Sizes of the subnetworks identified by the five approaches on Yao's dataset, myotubes.
Acknowledgments The bioinformatics analyses were performed on the Core Cluster of the  Institut Francais de Bioinformatique (IFB)  ( ANR-11-INBS-0013 ), on the  Centre de Calcul Intensif d'Aix-Marseille , and on the Genotoul cluster. We thank  Steffen Neuman  for his help for the submission of MOGAMUN as a Bioconductor package, and  Elisabeth Remy  ( Aix Marseille Univ, CNRS, I2M, Marseille Institute of Mathematics, Marseille, France ), and  Claude Pasquier  ( Univ. Nice Sophia Antipolis, CNRS, I3S, Nice, France ), for useful discussions.
Funding: EMNDT was supported by the  Mexican National Council of Science and Technology (CONACYT) ; This research and MT salary were supported by the  Excellence Initiative of Aix-Marseille University -A � Midex , a  French ''Investissements d'Avenir'' programme  (https:// www. univ-amu. fr/en/public/excellence-initiative)to AB. FM is supported by the  TRIM-RD  grant from  AFM Telethon  (https://www. afm-telethon. fr/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Literate computing has emerged as an important tool for computational studies and open science, with growing folklore of best practices. In this work, we report two case studiesone in computational magnetism and another in computational mathematics -where domain-specific software was exposed to the Jupyter environment. This enables high level control of simulations and computation, interactive exploration of computational results, batch processing on HPC resources, and reproducible workflow documentation in Jupyter notebooks. In the first study, Ubermag drives existing computational micromagnetics software through a domain-specific language embedded in Python. In the second study, a dedicated Jupyter kernel interfaces with the GAP system for computational discrete algebra and its dedicated programming language.
INTRODUCTION Research usually results in a publication that presents and shares the obtained findings and conclusions. For a publication to be scientifically valid, it must present the methodology rigorously, so that readers can follow the "recipe" and reproduce the results. If this criterion is met, the publication is considered reproducible. Reproducible publications are more easily re-usable and thus provide a significant opportunity to make (often tax-payer funded) research more impactful. However, the reproducibility of computational work is usually hindered not only by a lack of data or meta-data but also by a lack of details on the procedure and tools used: 1) The source code of the software used is not available. 2) Information on the computing environment, such as the hardware, operating system, supporting libraries, and (if required) code compilation details is not revealed.
3) The exact procedure which led to the results reported in the publication is not shared. This should include the set of parameters used, the simulation and data analysis procedure, and any additional data cleaning, processing, and visualization. Ideally, these are shared as open-source code and analysis scripts used to perform the simulation and to read, analyze, and visualize the resulting data. This way, the entire process can be repeated by re-running simulation and/or analysis scripts. A human-readable document detailing the computational steps taken, despite being "better-than-nothing", is still insufficient to ensure reproducibility, and keeping a detailed log of all steps taken during a computational study is often impossible. Reproducibility is a challenging question and spans a range of different topics.