dataesr/scientific-paragraphs-categorization huggingface dataset:
Importantly, these communities of editors have reached different levels of maturity. Some communities are quite mature, whereas others are still getting started and yet others fall somewhere in between. However, they all share the same tool for collaborative editing (MediaWiki; mediawiki. org) and the same basic rules to guide this cooperative editing: the "five pillars" of Wikipedia (tinyurl. com/bhs3m). As well, if we measure the global structure of these communities as a network in which the articles are the nodes and the hyperlinks to other articles connect these nodes, it seems to be approximately similar, at least for the largest Wikipedias  (Zlatic and Stefancic, 2011; tinyurl. com/andcqo7 ). In contrast to studies on open source software (e. g. ,  Crowston et al. , 2006: tinyurl. com/a3wec75; Koch, 2009: tinyurl.
Wikipedia data collection (outputs) As in prior studies of Wikipedia (e. g. ,  Wilkinson and Huberman, 2007: tinyurl. com/bjgge7x; Ortega et al. , 2007: tinyurl. com/auwcneq; Ortega et al. , 2009:  tinyurl. com/ bfb3spm), we relied on the database dumps published by the Wikimedia foundation. These databases contain complete records (date and time, author, etc. ) of every single contribution that comes in the form of a "revision" to any page in any of the 39 Wikipedias under study. Thus, it is possible to count the number of active editors per month and break them down in three groups, following the definitions offered by Wikimedia Foundation (stats. wikimedia. org/EN/): very active Wikipedians (those with 100 or more revisions in a given month); active Wikipedians (between 5 and 100 revisions in a given month), and other contributors (those with fewer numbers of edits in a certain month).
Findings An exploratory plot of our datasets shows a strong (but not perfect) correlation between the total number of Wikipedia contributors, the Internet population (Figure  1 ), and total tertiary-educated members of the population (Figure  2 ). Using the DEA model, we can identify different levels of efficiency in the conversion of these inputs to the Wikipedia community of contributors of different kinds. We first apply a constant return to scale model, then we introduce the possibility of a variation in return to scale. The results for this analysis are shown in Figure  3. The projects are listed in decreasing order of size. The bars indicate the relative efficiency. The longest bars, representing 100% efficiency, correspond to projects on the efficient frontier, that is, those that create the most outputs from their particular combination of inputs.
Network curation framework: implementing the fact sheet in Cytoscape To construct the influence network enriched with the genes responsive to EWS-FLI1 inhibition/re-expression from the fact sheet, we developed a software, integrated into the BiNoM Cytoscape plugin  (28). BiNoM is capable of processing the fact sheet described above in a self-consistent way, providing an interface to the user who decides on what level of abstraction to represent the entities (in the form of a family or an individual family members). At the second step of the pre-processing, the implicit reactions needed for consistent representation are added to the network, also under the user control. The actual facts sheet used for the Ewing's cancer network together with pre-processing protocol is provided in the web page of Supplementary Material ('Processing the fact sheet').
Network reverse engineering from siRNA silencing data In the first step, influences are inferred from siRNA/ RT-QPCR experiments. For that, a linear mixed model has been implemented in R (lme package), to determine linear dependence between presence of siRNA (two discrete levels) and gene expression, considering the fluctuations due to the difference between the clones and RT-QPCR measurement noise. All siRNAs significantly silenced their targets (P-value smaller than 1. 5 Â 10 À7 ). Therefore, this P-value was chosen as a threshold for identifying influences. All connections extracted from the literature (Figure  6A ) were confirmed by this method. In the second step, the inferred influences were separated into necessary and non-necessary connections, using the sub-network from Figure  6B.
Construction of an influence network describing EWS-FLI1 effects on cell proliferation and apoptosis based on literature data mining The influence network was reconstructed around the regulation of proliferation and apoptosis using EWS-FLI1-modulated genes. The list of 3416 modulated genes (selected above) was shrunk to the genes known to have a role in regulation of proliferation or apoptosis, according to GO  (26)  and BROAD/MSigDB databases  (27). This list was further reduced to 37 genes whose mechanisms of cell cycle and apoptosis regulation are clearly documented in the literature (top probesets of Supplementary Table  S1 , labeled by 'Net reconst'). Enriched pathways affecting proliferation/apoptosis and selected by GSEA were also included (highlighted in red in supplementary Tables  S2-S5 ).
The later step allows to handle gene families (i. e. E2Fs, IGFs) and to add implicit connections (for instance, CDK4 positively influences the (CDK4:CCND) complex formation) (see Network curation framework in Materials and Methods and Protocol 1 in the web page of supplementary material). The fact sheet was confronted to the TRANSPATH database  (36) , and missing links were manually curated and included. The advantage of this procedure is its flexibility: it is easy to update the fact sheet with new publications and to produce a new version of the network. The resulting influence network is shown in Figure  4A  and is accessible as a Cytoscape (37) session file, available at http://bioinfoout. curie. fr/projects/suppmaterials/suppmat_ewing_net work_paper/Supp_material/Network/Suppl_File_1_Net_ 1_CytoscapeSession.
Assessing completeness of the EWS-FLI1 signaling network: the concept of necessary connection In the previous paragraphs, experimental data were used to select genes and to validate their biological implications. However, the connections in the network were extracted from the literature that is not always dedicated to Ewing sarcoma. Genes like IGFBP3, MYC and Cyclin D are linked to EWS-FLI1 because these influences have been reported  (8, 9, 11, 14). However, several genes (E2F5, SKP2. ) are modulated by EWS-FLI1 but are not directly linked to EWS-FLI1 (Figure  4A ). Therefore, the network needs to be refined to match the context of Ewing sarcoma. To answer this question, we introduced the concept of necessary connection between genes. By definition, a necessary connection is such a regulatory connection between two molecular entities, which can be inferred from 'the data' but cannot be predicted from 'already existing network model'.
From its definition, a necessary connection always depends on (i) dataset, and (ii) already existing model. We provide in Supplementary Figure  S3  several examples of necessary connections (always applying the same definition), for various practical situations. For instance, the connection 'EWS-FLI1 ! CUL1' is necessary in our context (data and network) because (i) CUL1 is induced by EWS-FLI1 according to the transcriptome time series, (ii) no connection to CUL1 explains the transcriptional regulation of this gene in the network of Figure  4A. We decided to formalize this notion of necessary connection to handle the network model that can be incomplete (missing nodes and connections representing indirect effects). Subsequently, this definition was applied to all modulated genes in the network: the resulting necessary connections are listed in Table  3.
FOXO1A is regulated by E2F1 (49); IER3 is regulated by MYC, EP300, NFKB (RELA, NFKB1)  (50)  and CFLAR by NFKB (RELA, NFKB1) (51), and MYC  (52). E2F2 and E2F5 were also investigated, as they are both modulated by EWS-FLI1 and share similarities with E2F1  (53). The second step was to validate the results obtained in the transcriptional microarray time series on these regulators. Microarrays and RT-QPCR time series exhibited similar time profiles (Figure  2A  and Supplementary Figure  S6 ). In the third step, regulators were individually and transiently silenced in shA673-1C inducible cell line. Expression levels of FOXO1, IER3, CFLAR and all regulators were measured by RT-QPCR after each silencing experiment (Supplementary Table  S10 ). All these RT-QPCR data were semi-automatically analyzed by a reverse engineering method as following (see 'Network reverse engineering from siRNA silencing data' in Materials and Methods): (i) Identification of influences from experimental data (represented by all arrows of Figure  6B ).
This justifies improving our network in the future including miRNA data. With the aim to experimentally validate a subpart of our influence network, regulators of IER3, CFLAR and FOXO1 were investigated. Importantly, most of the influences, taken from the literature, on these three genes were confirmed using siRNA/RT-QPCR experiments (Figure  6B  and supplementary Table  S10 ). The influences of P300 on IER3 and E2F1 on FOXO1 were found to be repressive (activating according to literature). Therefore, these influences were modified accordingly to our experimental data to fit to the context of Ewing sarcoma. More interestingly, although P300 (in this study) and MYC (in this study and in the literature) repress IER3, IER3 most significant and yet unreported repressors are E2F2 and E2F5 (Figure  6B  and Supplementary Table  S10 ).
Therefore, this final network should be interpreted as the minimal one that reproduces the maximum amount of influences. We can suggest two methods for solving this problem of ambiguous interpretation: (i) extending experimental data by performing double-knockdown; (ii) comparing data to a mathematical model applied to the whole network, in a   S10 ). Blue arrows are based on RT-QPCR time series. Plain arrows represent transcriptional influences that are necessary for explaining data. Dashed arrows are questionable influences that can be explained through intermediate node. The arrow EWS-FLI1 -j FOXO1 is not necessary, although a recent article has identified it as a direct connection  (72). (C) The necessary connections shown in Figure  6B  have been compared with a subpart of the influence network (Figure  6A ).
ACKNOWLEDGEMENTS We thank  David Gentien  and  Laurent Daudet  for their help. A. Zinovyev  and  O. Delattre  are considered as joint last co-authors. FUNDING Institut National de la Sante´et de la Recherche Me´dicale; Institut Curie ;  Agence National de la Recherche  [ SITCON  project:  NR-06-BYOS-0004 ];  Institut National du Cancer  [ SYBEwing  project: 2009-1-PLBIO-04];  Ligue Nationale contre le Cancer  ( Equipe labellise´e and CIT program );  Re´seau National des Ge´nopoles; European Union  (APOSYS, KCK and EET pipeline projects); socie´te´F ranc¸aise des Cancers de l'Enfant and the following associations: Courir pour Mathieu, Dans les pas du Ge´ant,  Olivier Chape ,  Les Bagouzamanon  and les  Amis de Claire. The research leading to these results has received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013 )  ASSET  project  [FP7-HEALTH-2010-259348 ].
We found three populations of proteins: strongly competing, never competing, and interacting with different levels of strength. Populations and levels of strength are numerically characterized and provide a signature for the behavior of a protein in the crowded environment. We showed that partner identification, to some extent, does not depend on the competing partners present in the environment, that certain biochemical classes of proteins are intrinsically easier to analyze than others, and that small proteins are not more promiscuous than large ones. Our approach brings to light that the knowledge of the binding site can be used to reduce the high computational cost of docking simulations with no consequence in the quality of the results, demonstrating the possibility to apply coarse-grain docking to datasets made of thousands of proteins.
This leads to ask whether this failure comes from the fact that scoring functions, used to sort the docking solutions, are inefficient for partner identification or whether the difficulty comes from binding promiscuity between proteins in the cell that blurs the interaction signal of the functional partners. In the crowded cell, proteins experience non-specific and unintended interactions with the intracellular environment leading to a severe competition between functional and non-functional partners  [15] [16] [17] [18] [19]. This brings to light the importance of characterizing weak, potentially non-functional, interactions in order to predict functional ones and understand how proteins behave within a crowded environment  [16, 20, 21]. In this work, we tackle two distinct but related questions: (i) can a combination of coarse-grain docking and evolutionary information identify true interacting partners among a set of potential ones? (ii) what is the effect of binding promiscuity on a large and variate dataset of protein structures  [22] ? Previously, we have shown that knowing the experimental binding site of a protein can help to retrieve its native interacting partner within a set of decoys  [12].
To our knowledge, this is the first study performing a large-scale CC-D calculation, proposing an analysis of the binding promiscuity of the protein set, and providing to the scientific community the associated dataset of decoys  [30, 31]  at the same time. Previous large-scale analyses used docking by shape complementarity that quickly scans through several thousands proteins in a matter of seconds  [32, 33]  but ignore the electrostatic contribution playing however an important role in protein interactions  [34] [35] [36] [37]. We compared our method to two previously done studies  [32, 33]. Both of them do not perform a CC-D experiment, but a largescale analysis of selected protein pairs. Finally, we checked whether evolutionary information can be used to considerably restrict the number of docking interfaces to be examined and to render molecular computation feasible for a larger scale investigation of PPIs, based on thousands of proteins instead of hundreds.
Results The 168 proteins of the Mintseris Benchmark 2. 0  [25]  form 84 binary complexes known to interact in the cell. They cover three broad biochemical categories and three difficulty categories related to the degree of conformational change at the protein-protein interface. They are classified as Enzyme-Inhibitors (46 proteins), Antibody-Antigen  (20) , Antigen-Bound Antibody  (24) , Others (78), and also as Rigid Body (126), Medium  (26) , Difficult  (16). The set is constituted by 51 multimeric proteins and by 117 monomeric ones forming 41 complexes where at least one of the protein is multimeric. CC-D was realized on the full dataset from unbound structures, leading to 28224 docking simulations. Each calculation explored about 300000 ligand-receptor orientations, corresponding to ligand and receptor complete surfaces, and asked for more than 7 months computational time on WCG.
Finally, we demonstrate that binding site prediction is useful to discriminate native partners, but also to scale up the approach to thousands of protein interactions. This study is based on the large computational effort made by thousands of internautes helping World Community Grid over a period of 7 months. The complete dataset issued by the computation and the analysis is released to the scientific community. experiment assumes to know the residues belonging to the experimental interface of the proteins. This means that the residues lying at the interface of two proteins in a native complex are supposed to be known while no knowledge of the complex conformation is assumed. The second experiment replaces experimental interfaces by predictions of binding sites based on docking and evolutionary information.
This question is explored in ''Native interactions and competing partners''. Using predictions of interaction sites. Here, we drop the information on the location of the experimental interface of true partners and use predicted binding sites instead in order to evaluate our ability to retrieve the true partner in a totally blind experiment. Predicted interfaces are obtained by combining evolutionary information, computed with the program JET  [38]  (see Methods) and CC-D calculations: i. JET predictions are used to select a set of residues that are likely to belong to the real interface according to conservation and expected physico-chemical properties for interface residues (see Methods)  [38] ; ii. Early studies suggested that docking arbitrary partners together can nevertheless point to the correct interaction surfaces  [23, 39].
For this, they observed an accumulation of the docking solutions around the experimental location of the true partner. Following this approach, given a protein P, we used the information extracted from CC-D calculations involving all proteins in the database, to propose a set of residues that is highly likely to belong to the binding site of P. A score (called Normalized Interaction Propensity, or NIP, in Methods, Eq. (  7 )), associated to each residue in P, reveals the probability for a residue to belong to the real interface. For a given pair of proteins P 1 and P 2 , we evaluated all docking conformations by combining NIP residue scores and JET residue scores at the corresponding interface (this defines a FIR P1P2 as indicated in Methods, Eq. (  8 )) with the energy of the conformation (see Figure  3 ).
Intuitively, we select the conformation that shows highest NIP and JET scores at the interface together with a sufficiently low energy. The best conformation satisfies Eq. (1). As in section ''Knowing experimental interfaces'', the resulting II matrix is normalized into a NII matrix (see Methods, Eq. (  3 )). We analyzed partner prediction performances obtained by using NIP, JET or the combination JET+NIP (see Table  2  and Table  S9  in Text S1; see also Figure  S7 -S8 and Table  S10  in Text S1). When using only NIP, about half of the dataset (Enzyme-Inhibitors, Antibody-Antigens and Antigen-Bound Antibody) reaches an AUC of about 0. 60, while the other half (Others) shows a random behavior. The use of JET highly improves the performance of three quarters of the dataset (Enzyme-Inhibitors and Others) with an AUC increase of more than 10% (for each class), while for the remaining quarter, involving antibodies and antigens, the AUC drops to 0.
52. To take advantage of the different behavior of NIP and JET on different biochemical classes, we combined the two approaches. We obtained a global improvement for three quarters of the database (Enzyme-Inhibitors and Others) compared to the results of NIP alone (0. 77 and 0. 61, respectively), while increasing the performance for Antibody-Antigens (0. 58) and Antigen-Bound Antibody (0. 56) compared to JET alone. The poor performance obtained on Antibody-Antigens and Antigen-Bound Antibody possibly results from (i) a faster sequence evolution that blurs conservation signals leading to bad binding site predictions and (ii) from a large number of JET patches (corresponding to potential binding sites), compared to Enzyme-Inhibitors and Others, generating a large number of potential interactions that render more difficult partners discrimination.
By excluding Antibody-Antigen and Antigen-Bound Antibody from the dataset (see ''Enzyme-Inhibitors & Others'' in Table  2 ), the AUC reaches 0. 66 on the 124 remaining proteins. This is an encouraging outcome considering the absence of experimental information and the important number of competing proteins. Notice that on Enzyme-Inhibitors, the combination JET+NIP improves the already good JET performance to an AUC of 0. 77. In particular, JET+NIP obtains a decrease of less than 10% compared to predictions based on experimental data. See Tables 1, 2 and Table  S9  in Text S1 (for several performance measures and score thresholds). Finally, very weak interactions among Enzyme-Enzyme and Bound Antibody-Bound Antibody proteins are observable, as already noticed for docking based on experimental interfaces.
This is highlighted by the sub-matrices in Figure  S8  in Text S1 and constitutes another encouraging outcome for exploring interacting networks with docking based on interface predictions. Cumulative counts and percentages are displayed. The selected set of 56 monomers considered in  [33]  is also evaluated against the 168 proteins (fifth and sixth columns). The number of top proteins corresponding to the x% of the total number of proteins in the specified environment is given (second column). doi:10. 1371/journal. pcbi. 1003369. t001 Analysis on difficulty categories based on interface predictions. When using JET+NIP scores, the partitioning of the Mintseris dataset on Rigid, Medium and Difficult structures leads to 0. 60, 0. 68 and 0. 65 AUC values respectively (Table  1  and Table  S9  in Text S1).
Unexpectedly, Difficult and Medium perform similarly, and better than Rigid. This suggests that the interface prediction based on JET+NIP is robust to conformational changes that could occur upon complex formation, probably due to the fact that 1. JET is based on sequence information, and 2. JET is designed to predict surface residues that are possibly highly buried  [38] , and that can pass from a buried to an exposed state (and conversely) during the assembly. The Rigid class shows the worst performance, possibly due to the presence of Antibody-Antigen and Antigen-Bound Antibody in The analysis is realized by assuming knowledge of either the experimental interfaces or the predicted interfaces. Performance of partner prediction is evaluated through AUC values computed on the Mintseris dataset and its different subsets.
For each of the 84 native complexes, we generated 100 protein sets containing the desired complex and 19 other randomly chosen complexes (40 The protocol is based on docking calculations and JET predictions and produces an interaction matrix for the proteins in a database. Here, two protein structures, the receptor 1AY7 and the ligand 2MTA, are analyzed. The first step consists in cross-docking 1AY7 and 2MTA respectively, against all structures in the database (see cyan box for 1AY7 and blue box for 2MTA). A structure will be crossed dock against another in several conformations (from 100000 up to 450000, depending on the size of the proteins). In the schema, 1AY7 and 2MTA are also docked one against the other (see intersection between blue and cyan boxes). As a result of the cross-docking, a NIP score is associated to each protein leading to the prediction of an interaction site (color range from light blue to dark blue, corresponding to weak and strong signals respectively).
For the full dataset, the FIR values of the best conformation computed for each pair of proteins are recorded in the II matrix. Notice that the schema describing the computation for 1AY7 and 2MTA leads to one entry of the matrix. Finally, a normalization step produces the NII matrix used to discriminate potential partners. doi:10. 1371/journal. pcbi. 1003369. g003 proteins). Figure  4  illustrates the average and standard deviation of the distribution of IRs for the native complexes, ordered by increasing average rank. We can see that about the half of the native complexes are predicted in the top 10 (41 complexes over the 84), and 62% (52 over 84) are predicted on the top 15. This finding strongly suggests that these well-behaved complexes display some intrinsic properties leading to the correct prediction whatever the associated random set of potential partners is.