dataesr/scientific-paragraphs-categorization huggingface dataset:
First, the modeller must describe what is the expected behavior of the model for several conditions, based either on scientific literature or biological experiments. Some similar works using model checking to build and verify models, were recently published  [32, 33]. In order to standardize this process, we developed a tool, called MaBoSS_test, to easily verify if a logical model was coherent with specific biological assertions. This tool is based on MaBoSS simulation software, which produces simulations describing the evolution of the probability of states with time. Inspired from python's unittest library, we developed an extension for MaBoSS simulations which tests the validity of the dynamical behavior of the model via assert methods. Each assertion is used to verify if the model satisfies a given type of biological statement.
The function can be read as: "Assert that: after BRAF inhibition, using the initial condition for the colorectal cancer, IC_CRC, the probability of EGFR activation increases". If the "unit testing output" is selected, in the case that the assertion is not correct, the test will fail raising an exception. Otherwise, if the "detailed output" is selected, the result will be "True" or "False" and the probabilities to have EGFR active before and after BRAF inhibition will be displayed. This tool, its documentation and an example in the form of a Jupyter notebook are available on GitHub (https:// github. com/sysbio-curie/MaBoSS_test).
The mutations identified in the cell lines are interpreted using OncoKB database  [36] , an evidence-based repository with mutation annotations. Mutations referenced as loss-of-function (resp. gain-of function) are forced to 0 (resp. 1), thus constraining the possible transitions in the model as in The second strategy, preferably used with continuous data, is to modify the initial conditions and transition rates based on a continuous proxy for node activity. A cell line with a clear over-expression of a gene/protein, compared to the whole cohort of interest, will have the transition rate related to the activation (resp. inhibition) of the corresponding node favoured and made more (resp. less) likely (Fig  2E ). The initial probability that the node will be activated (i. e. , the probability to start at 1 among the 5000 stochastic trajectories) will also be modified accordingly, which is particularly important for input nodes that will not be regulated by the model.
In the absence of a specification, these nodes remain randomly fixed in active or inactive position at the beginning of the simulation (initial condition 0. 5). This method requires different conditions. First, a relevant node activity proxy has to be available in the data: it can be a level from transcriptomics, proteomics or even phospho-proteomics. Then, unlike mutations, the interpretation is not made absolutely but only in comparison to the other members of the cohort. In the present work, despite the proteic nature of most of the model nodes, RNA data will be used to perform continuous personalization and this for several reasons. First of all, RNA data are more frequently available in different databases, including GDSC, and offer a better coverage of the model nodes than RPPA data, which are often only available for a fraction of the nodes.
Interpretation of the same RPPA data, especially on phosphosites, is difficult and may require adaptation of the method at each node. Although it is an imperfect proxy of pathway activity, RNA has already been used in modeling approaches  [38, 39]. Note that the distribution of RNA levels is normalized between 0 and 1 on a gene-specific basis before being included in the model. This method therefore consists of integrating patient data into a logical model with a predetermined structure in order to impose patient-specific constraints on it. The parameters of the model are therefore not optimized on the basis of an objective function and the quantitative treatment response data are not used in the process. Only generic mechanistic knowledge, not specific to individual cell lines, has been used beforehand in the model construction and model-checking steps.
Drug and CRISPR/Cas9 screening In order to validate the relevance of personalized models to explain differential sensitivities to drugs, some experimental screening datasets are used. Drug screening data are downloaded from the Genomics of Drug Sensitivity in Cancer (GDSC) dataset  [40]  which includes two BRAF inhibitors: PLX-4720 and Dabrafenib. The cell lines are treated with increasing concentration of drugs and the viability of the cell line relative to untreated control is measured. The dose-response relative viability curve is fitted and then used to compute the half maximal inhibitory concentration (IC50) and the area under the dose-response curve (AUC)  [41]. Since the IC50 values are often extrapolated outside the concentration range actually tested, we will focus on the AUC metric for all validation with drug screening data.
AUC is a value between 0 and 1: values close to 1 mean that the relative viability has not been decreased, and lower values correspond to increased sensitivity to inhibitions. The results obtained with the two drugs are very strongly correlated (Pearson correlation of 0. 91) and the analyses presented here will therefore focus on only one of them, PLX-4720. Results of CRISPR/Cas9 screening are downloaded from Cell Model Passports  [34]. Two different datasets from Sanger Institute  [42]  and Broad Institute  [43]  are available. We use scaled Bayesian factors to assess the effect of CRISPR targeting of genes. These scores are computed based on the fold change distribution of sgRNA  [44]. The highest values indicate that the targeted gene is essential to the cell fitness. The agreement between the two databases is good  [45]  but we choose to focus on the Broad database, which is more balanced in terms of the relative proportions of melanomas and colorectal cancers.
The generic network presented in Fig  4  recapitulates the known interactions between the biological entities of the network. The network was first built from the literature, and then was verified and completed with potential missing connections using SIGNOR database  [55]. More details about the model can be found in the GINsim annotation file of the model  [56] , available in Supporting Information. A node representing Proliferation is also defined from ERK, p70 and p21. It is a coarse-grained representation of the model phenotype that can be compared with experimental data. It was also used to estimate some MaBoSS simulation parameters of the model. For instance, in the present work, all simulations have been performed with 5000 trajectories after verifying that this number was sufficient to ensure very low variability in the final results: 100 different simulations of the generic model, with 5000 trajectories each, result in an average Proliferation score of 0.
Differential sensitivities to BRAF targeting explained by personalized logical models Once the logical model consistency has been validated, personalized models are generated for each cell line by integrating their interpreted genomic features directly as model constraints or parameters. Their sensitivity to BRAF inhibition is then compared to experimentally observed sensitivities (Fig  5 ). In all the following analyses, we focus on three different personalization strategies using: only mutations as discrete personalization (Fig  5A  The first approach consists in using only mutations as discrete personalization (Fig 5A, upper row): the mutations identified in the dataset and that are present in the regulatory network are set to 1 for activating mutations and set to 0 for inactivating mutations.
In this case, the Proliferation scores from personalized models significantly correlate with both BRAF drug inhibitors (PLX-4720 and Dabrafenib) and both CRISPR datasets (using Pearson correlations). Note that the opposite directions of the correlations for the drug and CRISPR datasets are due to the fact that cell lines sensitive to BRAF inhibition result in low AUCs, and high scaled Bayesian factors, respectively, and, if the models are relevant, to low standardized Proliferation scores. Looking more closely at the corresponding scatter plot for PLX-4720 (Fig 5B, upper left panel), it can be seen that this correlation results from the model's ability to recover the highest sensitivity of the BRAF-mutated cell lines that form an undifferentiated cluster on the left side. These cell lines are indeed relatively more sensitive than non-mutated BRAF cell lines.
69, p. value = 0. 006). A similar analysis can be made of the impact of adding RNA data to personalization when comparing with the experimental response to PLX-4720 (Fig 5B, upper and lower left panels). Most of the non-sensitive cell lines (upper right corner) have not seen the behavior of the personalized models change with RNA addition. However, the numerous BRAF-mutated cell lines previously grouped around standardized Proliferation scores of 0. 5, are now better differentiated and their sensitivity predicted by personalized models has generally been revised towards lower scores (i. e. , higher sensitivity). Similar to the CRISPR data analysis, three sensitive cell lines have been shifted to the right and are misinterpreted by the model. As a result, the correlation restricted to BRAF-mutated cell lines is no longer significant (R = 0.
Comparison of the mechanistic approach with machine learning methods In order to provide comparison elements unbiased by prior knowledge or by the construction of the model, we performed some simple machine learning algorithms. Random forests have been fitted with inputs (mutations and/or RNA data) and outputs (sensitivities to drug or CRISPR BRAF inhibition) similar to those of logical models and the corresponding predictive accuracies are reported in S2 Fig. The first insight concerns data processing. The percentages of variance explained by the models are similar (around 70% of explained variance for drug sensitivity prediction) in the following three cases: unprocessed original data (thousands of genes), unprocessed original data for model-related genes only (tens of genes), and processed profiles of cell lines (tens of genes).
This supports the choice of a model with a small number of relevant genes, which appear to contain most of the information needed for prediction. Second, the absolute level of performance appears much lower for CRISPR (between 30 and 50%) probably suffering from the lower number of samples, especially in cases where the number of variables is the highest. This tends to reinforce the interest of mechanistic approaches that do not use any training on the data for smaller datasets, less suitable for learning. Finally, while mutations and RNA data seem to provide the same predictive power (especially for drugs), using the two together does not necessarily result in a better performance. Variable importance in these different random forests are reported in S3 Fig and are consistent with the analysis of mechanistic models.
It is therefore necessary to have a generic model that is both sufficiently accurate and broad enough so that the data integration allows the expression of the singularities of each cell line. If this is not the case, the learning of logical rules or the use of ensemble modeling could be favoured, usually including perturbation time-series data  [67]. It should also be noted that, in the BRAF model presented here, the translation of biological knowledge into a logical rule is not necessarily deterministic and unambiguous. The choices here have been made based on the interpretation of the literature only. And the presence of certain outliers, i. e. , cell lines whose behavior is not explained by the models, may indeed result from the limitations of the model, either in its scope (important genes not integrated), or in its definition (incorrect logical rules).
One perspective could even be to push personalization to the point of defining different types of data or even different personalization strategies for each node according to the knowledge of the mechanisms at work in the corresponding biological entity. A balance should then be found to allow a certain degree of automation in the code and to avoid overfitting. Despite these limitations, the results described above support the importance of combining the integration of different types of data to better explain differences in drug sensitivities. There was no doubt about this position of principle in general  [68] , and in particular in machine learning methods  [3, 69]. The technical implementation of these multi-omic integrations is nevertheless more difficult in mechanistic models where the relationships between the different types of data need to be more explicitly formulated  [8].
To conclude, we provide a comprehensive pipeline from clinical question to a validated mechanistic model which uses different types of omics data and adapts to dozens of different cell lines. This work, which is based only on the interpretation of data and not on the training of the model, continues some previous work that has already demonstrated the value of mechanistic approaches to answer questions about response to treatment, especially using dynamic data  [72] , and sometimes about the same pathways  [8]. In this context, our approach proves the interest of logical formalism to make use of scarce and static data facilitating application to a wide range of issues and datasets in a way that is sometimes complementary to learningbased approaches. (original data or processed data). Higher values of variable importance correspond to higher decrease in prediction performance when the variable is disturbed by permutation and therefore to variables with a positive contribution to predictive performance.
Funding: LC, EB and VN acknowledge support from  ANR  in the context of the project  ANR - FNR  project  AlgoReCell  [ ANR-16-CE12-0034 ]. This work was partially supported by the  European Union 's  Horizon 2020 program  (grant  826121 , iPC project). EB holds a chair at  Paris Artificial Intelligence Research Institute Institute (PRAIRIE ), funded in part by the  French government  under management of  Agence Nationale de la Recherche  as part of the  "Investissements d'avenir" program , reference  ANR-19-P3IA-0001  ( PRAIRIE 3IA Institute ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
All data files are available from the Cell Model Passports (https:// cellmodelpassports. sanger. ac. uk/downloads) and GDSC (https://www. cancerrxgene. org) portals. Code and data required to reproduce the study and all the plots are available in the following repositories: -Model-checking: https://github. com/ sysbio-curie/MaBoSS_test -Data, model personalization and analyses: https://github. com/ sysbio-curie/PROFILE_BRAF_Model The logical model can be found in the dedicated GitHub: https://github. com/sysbio-curie/PROFILE_BRAF_ Model/tree/master/Models/BRAF_Model and is deposited to model repository in GINsim webpage http://ginsim. org/node/248.
Background: Rare diseases (RDs) affect nearly 3 million people in France and at least 26-30 million people in Europe. These diseases, which represent a major medical concern, are mainly of genetic origin, often chronic, progressive, degenerative, life threatening and disabling, accounting for more than one third of all deaths occurring during infancy. In this context, there are needs for coordinated information on RDs at national /international levels, based on high quality, interoperable and sharable data. The main objective of the RaDiCo (Rare Disease Cohorts) program, coordinated by Inserm, was the development of RD e-cohorts via a national platform. The cohort projects were selected through a national call in 2014. The e-cohorts are supported by an interoperable platform, equivalent to an infrastructure, constructed on the "cloud computing" principle and in compliance with the European General Data Protection Regulation.
It is dedicated to allow a continuous monitoring of data quality and consistency, in line with the French Health Data Hub. Results: Depending on cohorts, the objectives are to describe the natural history of the studied RD(s), establish phenotype-genotype correlations, decipher their pathophysiology, assess their societal and medico-economic impact, and/or identify patients eligible for new therapeutic approaches. Inclusion of prevalent and incident cases started at the end of 2016. As of April 2021, 5558 patients have been included within 13 RD e-cohorts covering 67 diseases integrated in 10 European Reference Networks and contributing to the European Joint Program on RDs. Several original results have been obtained in relation with the secondary objectives of the RaDiCo cohorts. They deal with discovery of new disease genes, assessment of treatment management, deciphering the underlying pathophysiological mechanisms, diagnostic approaches, genotype-phenotype relationships, development and validation of questionnaires relative to disease burden, or methodological aspects.
Identifying the locks is essential. Yet, RD clinical management and research in France and Europe have been hampered by a lack of resources at several levels: few scientists work on only one given disease; few patients per disease and patients are scattered over large geographic areas, causing di culties in gathering data on their disease; existing databases as well as biological collections, when existing, are usually local, small, incomplete, not always quality-controlled, of heterogeneous formats and contents, and are rarely accessible or standardized for allowing interoperability; phenomes are often complex and partially described along with time, with insu cient interdisciplinary cooperation. The need to promote networks of expertise for RDs in order to improve both RD clinical care and research has been considered a priority in France since 1995.
Indeed, RDs occupy an important place in public health in France: Orphanet (an informational RD website and a directory of expert services, launched in A need for launching RD cohorts. In this fast-mutating context, RD professionals highlighted the critical need to implement nation-wide, multidisciplinary, high-quality cohort studies in order to address key scienti c and medico-economic questions. In this context, the request was high to get access to appropriate resources, methods and tools for collecting RD data at the (inter)national level. Given the speci cities of RDs -limited number of patients per country, scarcity of relevant knowledge and expertise, and fragmentation of research -they have been considered as a distinctive domain of very high national and European added value. A supporting research program was therefore required to provide essential information on disease history and characteristics, and to foster the identi cation of underlying molecular mechanisms, genotype/phenotype correlations.
New knowledge would ultimately lead to better targeted care and treatments. The structuring boost given by the PNMR2 was considered as an opportunity to make collective efforts for building RD cohorts and therefore to propose the RaDiCo (Rare Disease Cohort) project to the national call on cohorts of the rst Investments for the Future Program The objective of the RaDiCo project was twofold. On the one hand, the scienti c objective was to set up several RD e-cohorts with the following aims according to the idiosyncrasy of each cohort: Describe the natural history of the targeted RDs; Establish genotype-phenotype correlations; Decipher the underlying pathophysiological mechanisms; Identify new therapeutic avenues; Estimate their societal and medicoeconomic impact; Identify patients eligible for new therapeutic approaches; De ne a methodological strategy of analysis for cohorts including both prevalent and incident cases (with respect to modeling and bias analyses).
On the other hand, to reach these scienti c objectives, another goal was to build a national operational platform, equivalent to an Infrastructure as a Service (IaaS), for implementing a potentially unlimited number of e-cohorts consisting of prevalent and incident cases. Such cohort projects had to be closely articulated with the above-mentioned established networks on RDs. The PNMR2 also fostered the development of the National RD Data Bank (BNDMR standing for "Banque Nationale de Données Maladies Rares", Figure  1 ). The BNDMR was built as part of CEMARA [ 8 ] ("Centre Maladies Rares"), which started in 2007 at Necker Enfants-Malades Hospital (AP-HP). The BNDMR, which is dedicated to public health issues, aims at collecting general epidemiological and public health data on all patients with a RD in France, on the basis of a common RD minimum data set [ 9 ] and a unique identi er [ 10 ] for each RD patient.
Results Thirty-three letters of intent were received after the publication of the RaDiCo call for RD cohorts (see Material and Methods). This call led to select 16 national and/or European RD cohort projects on July 15th, 2014. Among these, 3 have been discontinued, secondary to decisions of the Scienti c and Plenary committees of the RaDiCo program (see Material and Methods): after demand from the principal investigators (PIs) for two of them since the cohorts had not started, and another in 2019 since it could not start in due time. The groups of RDs targeted by the 13 current cohorts appear in Table  1. The general framework of the RaDiCo cohorts is presented below (Table  2 ), with or without associated biocollections at each site, together with the planned inclusion and follow-up period.
The start of the inclusions and the inclusion targets are mentioned. All cohorts are multicentric, mainly national but also European (SEDVasc, ECYSCO) and international (GenIDA). For all the cohorts, a total of 11,650 included patients are expected at the end of the inclusion period (July 2027). As of April 2021, 5558 patients had been included into 13 RD e-cohorts, covering 67 diseases from ~300 a liated expert centers (Figure  2 ). Each implementation step of the e-cohorts is presented in Figure  3. These cohorts were supported by a platform enabling information ow and communication, which has been set up in the framework of the RaDiCo program. This platform, which ful lls the necessary requirements of an Information System (IS), has been assessed by an independent external auditor. Moreover, it contributed to the design of the IS from the Inserm "France Cohorts" program designed to support not only RD e-cohorts, but also cohorts of patients with common multifactorial disorders, as well as population-based epidemiological cohorts.
Methodological aspects -Several tools for harmonizing our approaches in our own country and in Europe have been developed. We de ned a way to federating RD patients identities  [10] ; to develop Cerberus, an access control scheme for enforcing least privilege in patient cohort study platforms for the RaDiCo-GENIDA cohort [ 31 ]; to propose an overview of the current situation and experiences of the national RD registries in Europe: [ 32 ]; with other European colleagues to share recommendations for improving the quality of RD registries [ 33 , 34 ] the two latter papers were used for the design of our e-Health approach. Moreover, RaDiCo is involved in the French PNMR3 and contributes to the EJP-RD program [ 35 ].