dataesr/scientific-paragraphs-categorization huggingface dataset:
The rewritting strategy was applied to the PID, KEGG and ACSN BioPAX models to construct three Cadbiom guarded transition models of large-scale regulatory and signalling networks. These Cadbiom models were obtained by using the package biopax2cadbiom to interpret BioPAX databases. The results of the conversions of the KEGG and PID databases extracted from Pathway Commons, and the ACSN database are available on the website http:// cadbiom. genouest. org/doc/cadbiom/workflow_overview. html#prebuilt-models. As shown in Table  2 , the Cadbiom models contained slightly fewer entities than the BioPAX models: there are 7%, 26% and 13% fewer entities in the PID, KEGG and ACSN Cadbiom models, respectively. The ratios between the number of transitions in the Cadbiom model (see Table  2 ) and the number of reactions extracted from the BioPAX resource (see Table  1 ) are 1.
PID The comparison between ACSN and PID database contents has been previously published  [21]  and the authors concluded to a low overlap though the most canonical molecular pathways are represented in both PID and ACSN databases. Consistent with this, while the comparison between PID and ACSN Cadbiom models showed similar enriched GO terms, we observed differences in specific pathways such as the leptin signaling pathway, the IL27RA signaling pathway, the IL23R signaling pathway, the Neurotrophic factor mediated Trk signaling pathway and the Circadian rhythm pathway which are present in the PID model but absent in the ACSN model (see details in S1 Appendix). On the other hand, some specific processes were enriched in ACSN compared to PID, such as the senescence network which is not specifically documented in PID.
It is difficult to compare an "ACSN module" with a PID pathway because the module gathers more information. For example, the canonical WNT module in Table  2. Characteristics of the Cadbiom models obtained after the conversion of BioPAX sources into models with guarded transitions. The Cadbiom models are described by entities, events and transitions. They are compared to the numbers of entities and reactions in the BioPAX models. The boundary entities correspond to the peripherical entities of the model. They are described according to their type and their role in the cadbiom model. ACSN contains 200 proteins while the WNT signaling pathway in PID contains only 28 proteins.
As previously described  [6] , a phenotype is modeled by a state of the system, called query, described by a logical formula involving biological entities of the Cadbiom model. The controllers of the phenotype are all boundary entities of the model which are activated in at least one dynamical simulation of the model leading to the activation of the phenotype according to the guarded transition semantics. The Trajectory entities of the phenotype are all intermediate entities activated in at least one trajectory (see Materials and methods for details). Causality analyses of phenotypes in the PID and ACSN models highlight the complementarity of the two databases. We used the mapping of the Cadbiom package to manually extract the Cadbiom identifiers from each of the HUGO gene identifiers for the 27 EMT genes shared by ACSN and PID databases.
When several entities in the database could be mapped to a HUGO identifier, we considered all these entities. A Boolean formula for each of these identifiers was constructed. We performed causality searches to explore the dynamics of the PID and ACSN Cadbiom models for each of the Boolean formula by using the cadbiom-cmd package. It is important to note that each model has its own node description and manual curation is required to annotate them with HUGO identifiers. Therefore, automatic merging BioPAX files from the PID and ACSN databases is not possible. Fig  6A  shows The third column describes the number of trajectories leading to the phenotype. The fourth column describes the number of controllers. The fifth column describes the number of nodes in all trajectories leading to the phenotype.
The remaining 15 genes are associated with fewer than 3 trajectories (uncolored rows). In contrast, the EMT genes in the ACSN model are globally associated with more trajectories (400 for ACTA2, CXCL12, ITGB5, MMP1, MMP2, SERPINE1 and VEGFA, 308, 114 and 96 for TIMP1, SNAI2 and MMP14, respectively, green rows in Fig 6A ). The remaining genes are associated with a wide range of trajectory numbers ranging from 1 to 31 (grey and uncolored rows) and only 4 genes have less than 3 trajectories (uncolored rows). This is consistent with the fact that the ACSN (Atlas of Cancer Signalling Network) database focuses on pathways involved in cancer where the epithelio-mesenchymal transition plays a critical role and is represented by a special map  [21]. Consistent with this, the EMT genes of our panel are likely to be more documented in this database than in the generic PID database.
We observed that the genes associated with a large number of trajectories (green rows in Fig 6A, greater than 40 trajectories) are also the genes for which the number of controllers is greater than 40 (except for MMP14 in the ACSN database). For genes with few trajectories, the association between the number of controllers and the number of trajectories was more variable: for instance, the SNAI2 gene has only one trajectory that contains 8 boundary entities in the PID model. We calculated the ratio between boundary entities and the number of controllers to investigate the role of trajectory entities. We observed that the variation of this ratio is similar between the two models, from 1 to 6 (2. 88 ± 0. 96) in the PID model and from 1 to 4. 6 with a high number of trajectories (from 96 to 400), grey rows correspond to phenotypes with an intermediate number of trajectories (from 6 to 42) and uncolored rows correspond to phenotypes with less than 3 trajectories.
This example illustrates the combinatories of controllers which can be either signaling molecules or transcriptional regulators but also complexes associating a growth factor with its receptor or two transcriptional regulators. The more information the database contains about a biological mechanism, the more controllers there are in the trajectories; and the more controllers are shared by different mechanisms, the more combinatories they are. To illustrate this, the contribution of the growth factors PDGF and TGFβ in the regulation of SPP1 in the ACSN model was described by three trajectories differing only in the TGFβ family members that include TGFβ1, TGFβ2 and TGFβ3 (S1 Appendix).
In this context, we analyzed trajectories of gene expression regulation, specific to epithelial or mesenchymal states, using large-scale networks. According to  [27] , the epithelial phenotype is characterized by the expression of PERP which is an essential component of desmosome junctions and has been shown to preserve epithelial integrity. Similarily, according to  [28] , the mesenchymal phenotype is characterized by the expression MMP2 which is a metalloproteinase mainly expressed by mesenchymal cells that promotes cell invasion. However, we noticed that the ACSN model does not contain the PERP gene, which is consistent with the fact that the ACSN database is focused on cancer pathways. Therefore, we chose the PID model to characterize the regulatory mechanisms of genes associated with epithelial and mesenchymal phenotypes.
We used the causality search module to find the trajectories to activate either PERP alone, or MMP2 alone or MMP2 and PERP simultaneously in the Cadbiom model built from the PID database. In order to limit the computing time, we focused the analysis on the first 400 trajectories and evaluated the composition of these trajectories. All biomolecules present in all trajectories were analyzed using a clustering approach and heat-maps allowed us to easily compare the composition of the trajectories. In addition we used a graphical representation of all trajectories where nodes are biomolecules or reactions when there are more than one reagent or one reactant. The Cadbiom signatures of the epithelial marker PERP and the mesenchymal marker MMP2 are specific. The PERP query returned 10 trajectories that contained 17 controllers.
These trajectories not only confirm the ATF2-dependent regulation of MMP2  [35, 36]  but also provide new information upon the combination of kinases that were not included in the model. In support of this, we observed that the presence of JNK, p38gamma and MAPK14/ERK2 were mutually exclusive for the design of three types of regulatory pathways. As shown in the heatmap (Fig  7C ), many controllers are not included in the 86 trajectories characterized by the presence of JNK or p38gamma suggesting specific regulation. These include PAK1 and PAK2, and the complex RAC1-CDC42-GTP (black dotted line) that exemplify the signalling pathway known to promote PAK-dependent EMT  [37]  and that has recently been implicated in the regulation of MMP2  [38]. Of note, the involvement of PAK1-dependent signalling in the regulation of MMP2 was not described in the PID database but revealed by the PID Cadbiom model.
Discussion and conclusion The understanding of molecular mechanisms underlying complex biological processes relies today on the use of databases and computational methods for large-scale networks. The diversity of both languages used to formalize biological events, and tools used to model them, confronts the biologist with choices that must take into account the type of data and the question asked. Considerable efforts have been made to standardize formalisms and BioPAX is now a widely used language for representing biological pathways  [7]. Because of its interoperability, the BioPAX language can facilitate the integration of data from different sources. However, to our knowledge, there is only one tool for simulating biological pathway models specified in BioPAX, BioASF, a framework based on the principles of discrete event systems and multi agent systems  [17].
Although this tool can simulate the models described in BioPAX, the need to manually define its own simulation rules for each model limits its use to models that are already finely described and of very small size. For the first time, we developed a framework that enables the dynamical modeling of largescale models, formalized in the BioPAX language. A main contribution of this work is the redesign of an earlier version of the Cadbiom tool  [6]  which includes a module to convert a database described in the BioPAX format into a dynamical model based on guarded transitions, a module to explore and to identify the trajectories and controllers with respect to an expected phenotype (e. g. , gene activation) and a module to facilitate visualizing analyses by graphs and heatmaps. Applied to the PID and KEGG BioPAX files from the Pathway Commons database and to the curated ACSN BioPAX file, we compared the structures of the resulting models and demonstrated their high level of complementarity.
High level of complementarity in the BioPAX content Investigating the structure and the content of the BioPAX files reveals disparities between the PID, KEGG (extracted from the Pathway Commons database) and ACSN files. The differences in the type and number of BioPAX classes highlight that KEGG is a metabolism database with metabolic reactions and an abundance of small molecules. It should be noted that the original KEGG database encompasses not only metabolism pathways but also many other biological pathways  [49]  that do not appear to be available as BioPAX files in the Pathway Commons database. On the other hand, the PID and ACSN models share many features including the number of transitions and boundary entities. Finally, the analysis of the controllers allowed us to discriminate between the PID and ACSN models, revealing different dynamics.
Towards the combined analysis of BioPAX databases Although the three BioPAX databases we analyzed are very complementary, several limitations prevented us from merging the databases to take full advantage of their complementarities. The main limitation is related to the lack of standardization in the naming of several entities, such as complexes that do not follow a unified framework for their identifiers. Special attention should be paid to the fact that BioPAX models are often incomplete. A molecule can be associated/transformed into different complexes with different locations, which prevents automatic model merging. Our strategy has been to work with unified graphs for each model rather than fusioning models. The most promising approach to combine BioPAX models into a single Cadbiom model is to adopt a strategy similar to the method implemented for the construction of the OmniPath database  [15]  which aggregates multiple databases into a unified interaction graph of signaling pathways.
In this paper, the authors manually selected the contents of the databases corresponding to standardized entities and focused on a limited but well-curated number of reactions and classes of biological entities. The result is large-scale interaction graphs, which, however cannot be analyzed with dynamical methods: as shown in  [16] , this resource seems to be of great interest to manually build custom Boolean models, but requires an important manual curation procedure to interpret the OmniPath interaction graph into a Boolean model. Our plan is to adopt a strategy similar to  [15]  to extract information from multiple BioPAX resources on a subset of typed entities. This will provide us with a unified data source that will automatically be then interpreted in a dynamical Cadbiom multi-source model.
The epithelio-mesenchymal transition as a paradigm of the complexity of regulatory pathways EMT is orchestrated by numerous changes in regulatory pathways  [19]  and understanding how these molecular pathways act together requires an integrative and dynamical view that considers combination, competition and control events in large-scale models. Using the Cadbiom model built from PID knowledge encoded in the BioPAX format, we identified a large number of controllers for the EMT markers, PERP and MMP2. Importantly, the predicted controllers in the trajectories for PERP and MMP2 were supported by the published literature (21 Pubmed references) and only 3 references were initially present in the PID database that contains 4890 references. This demonstrates the predictive value of the Cadbiom models.
Applying Cadbiom to other BioPAX databases The current release of the Pathway Commons database (Version 12) contains 24 databases that all have a BioPAX file that could be converted in a Cadbiom model with a similar approach. However, this requires a manual analysis of the internal structure of each database to identify the keys for conversion to a Cadbiom model. In this case, a special attention must be paid to the use of nested classes to represent biological processes. These classes are described by generic entities that themselves contain other generic entities and require specific curation procedures in order to interpret the model dynamically. In the models we considered, these nested classes and the underlying generic entities could be totally expanded in a two-step procedure. By contrast, we observed that, the Reactome pathway database  [52]  contains 13.
5% nested entity classes among its 42,349 physical entities, so their expansion cannot be achieved with a naive strategy. Based on the present work, our future plan is to apply the Cadbiom framework to the Comparative Toxicogenomics Database (CDT)  [53]  which provides a very broad range of biological toxicology information including chemicals, genes, proteins, phenotypes, pathways, diseases, and their relationships. The BioPAX CTD model is available on the Pathway Commons website and the interpretation of the BioPAX CTD model in the CTD Cadbiom model will allow us to explore how environmental exposures affect health.
To our knowledge, these models have not yet been tested in the case of sea state downscaling. In this study, a convolutional neural network (CNN)-type model for the prediction of significant wave height from wind fields in the Bay of Biscay is presented. The performance of this model is evaluated at several points and compared to other statistical downscaling methods and to WAVE-WATCH III hindcast databases. The results obtained from these different stations show that the proposed method is suitable for predicting sea states. The observed performances are superior to those of the other statistical downscaling methods studied but remain inferior to those of the physical models. The low computational cost and the ease of implementation are, however, important assets for this method.
Then, its performances are compared to two classical statistical downscaling methods. Finally, the prediction made by the neural network trained using buoy data and the prediction from a numerical model are compared. The Bay of Biscay has been chosen because of the availability of the hindcast database. This region is characterized by a significant proportion of multimodal sea states with swells generated all across North Atlantic Ocean and wind sea waves that can be significant. It was developed in order to characterize precisely the near-shore sea state conditions for the production and design of marine renewable energy (MRE) devices, such as wave energy converters (WECs;  Payne et al. , 2021). The proposed methodology could be extended for any other locations, as soon as sufficient data are available.
Climate Forecast System Reanalysis The Climate Forecast System Reanalysis (CFSR) is a hindcast database  (Saha et al. , 2010). Wind and other environmental variables (humidity, pressure, etc. ) are recomputed with a homogeneous model and data assimilation system to eliminate fictitious trends caused by model and data assimilation changes in real time. The main purpose of this type of treatment is to obtain consistent datasets for the study of climate. Only 10 m wind components U 10 and V 10 are extracted with a degraded space-time resolution of 3 h for the time resolution and half a degree in latitude and longitude for the spatial resolution. The predictors of the proposed method are derived from these components. Their construction is detailed in Sect. 3. 1.
In this study, data from two hindcast databases using the WAVEWATCH III model are exploited as follows: -HOMERE. This specific hindcast database has been developed by Ifremer (French National Institute for Ocean Science) for the "assessment of sea-state climatologies to support research and engineering activities related to the development of marine renewable energies over the Bay of Biscay and the Channel"  (Boudière et al. , 2013). This model takes realistic sea bed roughness mapping into account. The forcing considered includes CFSR wind, water levels, and tidal currents computed using the MARS 2D hydrodynamic model  (Lazure and Dumas, 2007). The HOMERE mesh consists of an unstructured grid built from the depth and wave propagation velocity. The nodes are spaced up to 10 km offshore and only 200 m near the coast.
-RESOURCECODE (or RSCODE). This hindcast database is the result of a consortium of academic expertise, industry leaders, and research centers to which Ifremer belongs. The development and expansion of the HOMERE MetOcean hindcast database is a central point of this project. Unlike the latter, the wind forcing is derived from the ERA5 database. Among the parameters describing the sea state, it was decided to focus only on the H s , which, therefore, constitutes the predictand.
Convolutional neuronal network Deep learning techniques are promising approaches for statistical downscaling because of their ability to learn spatial features from huge spatiotemporal datasets. The model presented in this paper is a convolutional neural network (CNN). It is inspired by the CNN-PR (pattern recognition) network, using standard topologies from pattern recognition for precipitation downscaling presented in Baño-Medina et al. (  2020 ). The use of CNN for the statistical downscaling of the sea state has been motivated by properties of CNN that are interesting for the prediction of wind generated waves, as follows: -Shared-weight architecture of kernels. For each filter of the convolution layer, it is the same kernel that runs through the input matrix. Thus, when a meteorological event of the same magnitude appears at one position or another on the projected and time-shifted wind map, the result of the convolution operation on the output layer will be the same.
Architecture Sea state is the result of the combination of wind sea wave and swell. Thus, the model needs to take both wave systems into consideration. This is realized by implementing a hybrid CNN model, HCNN, which takes two inputs, i. e. , the global and local predictor. Figure  3  shows a schematic view of the network architecture. This neural network has been implemented with the Python deep learning library Keras (https://github. com/ keras-team/keras, last access: 5 October 2021). The different layers used in the model are as follows: -ZeroPadding2D. This adds rows and columns of zeros at the top, bottom, left, and right side of the input to conserve its spatial dimensions (height and width) during the convolution operation. -Conv2D. This applies the convolution operation. -BatchNormalization.
Evaluation of the method in the Bay of Biscay This neural network was trained using data from a point in the Bay of Biscay (located at coordinates 45. 25 • N, 1. 61 • W), belonging to the mesh of the HOMERE database (node no. 7818). This point is located on the continental shelf at a depth of about 55 m. In Sects. 4 and 5, the H s of HOMERE is chosen as the reference value and is referred to as the target H s. The time series of H s is presented on the top panel of Fig. 4. The mean value of the target H s is 1. 92 m (min 0. 25 m; max 10. 56 m).
Metrics To evaluate the performance of downscaling methods, classical metrics are used. T and P denote, respectively, the value of the target H s and the predicted one. The cardinal of the time series is n. The root mean squared error (RMSE) is the measure of the accuracy, as follows: RMSE = (T -P ) 2 /n. (3) The Pearson's correlation coefficient is the linear correlation between two datasets, as follows: ρ = [(T i -T i /n)(P i -P i /n)] (T i -T i /n) 2 (P i -P i /n) 2. ( 4 ) The scatter index is the prediction error normalized by mean of observations and debiased, good metric to quantify the dispersion of the scatterplot and to compare performances at different stations, as follows: The RMSE of errors greater than the 95th quantile of the error is as follows: ERR ABS = ABS(T -P ) (6) RMSEQ95 = ERR ABS [ERR ABS > 95 • percentile of ERR ABS ] 2 /n.
Cross-validation The performance of the HCNN model is evaluated through seven-fold cross-validation. To do so, the dataset is divided into seven periods of 3 consecutive years. Each of these periods is then successively taken as a validation dataset, with the rest of the data being used for learning. For each of these iterations, five models are trained, and the grand mean of each score is computed on each fold. The training of five individual models is a robust approach to evaluating the skills of deep learning models that are stochastic because of their use of randomness during training, particularly because of the initialization to a random state and the use of stochastic gradient descent algorithms. According to Fig. 5 , HCNN gives results of the same order of magnitude over each validation period.
The variations observed are justified by the differences in climatology specific to each period. The standard deviation of the HOMERE H s over the validation period (see Fig. 6 ) is indeed correlated with the RMSE (ρ: 0. 65). Since the performances of the HCNN model are homogeneous on each of the folds, only the last one is kept to conduct an in-depth study of the performances of the model. The selected training and validation datasets are as follows: -Train -13 January 1994 to 31 December 2013 (size of 58 341) -Validation -1 January 2014 to 31 December 2016 (size of 8767).