dataesr/scientific-paragraphs-categorization huggingface dataset:
Each continent is further divided to 'countries' (that is, modules). Such structure allows visualization and exploration of either the whole network or its constitutive subparts (Figure  2 ). ACSN represents a large biochemical reaction network of 4826 reactions covering 2371 proteins, 5975 chemical species and supported by information from 2919 articles (Table  1 ). ACSN is manually constructed using standard graphical notation. ACSN is based on the information about molecular mechanisms manually extracted from scientific literature and external databases by map managers. The maps are constructed using Systems Biology Graphical Notation language  7  as it is implemented in CellDesigner tool 8 that ensures compatibility of ACSN maps with various tools for network analysis, data integration and network modeling.
Hierarchical organization of ACSN (Figure  1 ) reflects the principle of literature curation for its construction. The major review papers in the field were the basis for defining the borders of the ACSN maps. The key canonical pathways, as they are depicted in the major pathway databases, represent the basis for depicting modules in ACSN, reflecting the consensus view of the scientific community on the pathways. The most detailed level of ACSN depicts information extracted from the recent literature with the requirement that the interactions and processes included to this level of ACSN are supported by several (at least two) independent investigations. The process of construction and curation of ACSN maps, including graphical standard, data model, rules of literature selection, data input from other databases and assignment of confidence scores, are detailed in the Materials and methods section.
Each map in ACSN covers hundreds of molecular players, biochemical reactions and causal relationships between the molecular players and cellular phenotypes. Each molecular player and each reaction on the map is annotated with the PubMed  references and cross-referenced with other molecular biology databases. The notes of map managers can be added (Figure  2 , Supplementary Figures  S2  and S3 ) in a free format. After the construction, the maps are critically reviewed by the experts in the corresponding fields. Exchange of information with experts is facilitated by an associated web-blog, where comments regarding each component of the map can be posted (Supplementary Figures  S2  and S3 ).
In cancer, EMT promotes invasion of tumour cells into surrounding stromal tissues. Description of cell motilityrelated processes corresponds to 'Activating invasion and metastasis' cancer hallmark. In ACSN, the EMT and cell motility map is composed of five interconnected functional modules with the core EMT regulators placed in the centre of the map. Graphically, the map represents a cell directly interacting with a neighbour cell and with extracellular matrix. ACSN web interface: access, navigation, visualization, and maintenance The ACSN website provides extensive documentation on ACSN, including online tutorials, user guides and videos. The content of the atlas is available for downloading in several exchange formats (see Materials and methods). The user-friendly interface connects together the global Atlas map, the individual maps of biological processes and the functional module maps.
This problem is addressed in ACSN in four complementary ways. First, exploration of the content is facilitated by the hierarchical modular structure of ACSN. Shuttling between the global ACSN map to individual maps of biological processes and further to functional module maps is possible. It allows visualization of only a limited number of molecular species and reactions related to a particular molecular mechanism (Figures  1, 2b  and c ). Second, the semantic zooming feature in ACSN simplifies navigation through the large maps of molecular interactions, showing readable amount of details at each zoom level. Gradual exclusion of details allows exploration of map content from the detailed towards the top-level view (Supplementary Figure  S4 ). Pruning of maps helps to eliminate non-essential information, highlighting the core processes corresponding to the canonical pathways.
The principle of pruning can be understood from the ACSN representation of the Cell cycle map or from the mitogenactivated protein kinase (MAPK) module of Cell survival maps (Supplementary Figure  S5 ). Third, to facilitate exploration of individual entities of the map, the function of selecting and highlighting only species of interest is available in the ACSN user interface (detailed in the user guide at the ACSN website). It is also possible to select and highlight neighbours of a molecular species of interest. This way, the connectivity of the selected entities through the map can be studied. Step-wise enlarging of the neighbourhood coverage can help to follow the propagation of signalling from one molecular entity to another (Supplementary Figure  S6 ). The fourth way to explore the map is via detailed annotation post of each component of the map that can be accessed through the callout window or from the selection panel (Figure  2  and Supplementary Figure  S3 ).
Annotations contain extended information on the entities, such as involvement in ACSN maps and modules; participation in complexes; participation in reactions and their regulation and so on. Each component listed in the annotation post is a hyperlink leading to the corresponding map entity. Shuttling between the annotation post and the map facilitates the exploration of ACSN. In addition, annotation posts contain links to directly access external resources. Finally, users can leave comments via an integrated blog system that helps to map maintenance (Supplementary Figures  S2  and S3 ). The field of oncology evolves rapidly, therefore ACSN maps require regular updates. The process of maintenance involves map managers that regularly collect the users' comments posted in the blog of ACSN, check the latest scientific literature and update the maps and the annotations accordingly.
An automated procedure supports update of ACSN and archives older versions of posts including users' comments, thus providing traceability of all changes on the maps and all discussions in the blog (Supplementary Figures  S2  and S3 ). Comparison of ACSN to other pathway databases. Because of its focus on cancer-related processes, ACSN is smaller in absolute size compared with other general-scope pathway databases (Supplementary Table  S1 ). ACSN does not cover as wide range of cell signalling and metabolic pathways as REACTOME  37  or KEGG PATHWAY. 38 However, one can suggest that focussing on fewer cellular processes allows performing a more detailed description of molecular mechanisms resulting in a denser and richer interaction network. We compared ACSN with two pathway databases that have similar structure and level of details: REACTOME  37  and National Cancer Institute Pathway Interaction Database (NCI PID).
25  BiNoM plugin of Cytoscape was used to process BioPAX 3. 0 files,  39, 40  storing the content of the three pathway databases compared. We only used protein-related parts of each database and excluded the metabolic reactions involving small molecules only. We have found that each pathway database among the three is characterized by a specific distinguishing feature. REACTOME is the largest among the three pathway databases in terms of the number of proteins and reaction. However, ACSN is almost three times denser in terms of the number of reactions per protein and is comparable to NCI PID in this aspect. NCI PID is the densest among the three databases in terms of the number of protein complex species described per protein (Supplementary Figure  S7A ). We further extracted only the reaction graphs from the three databases and analysed their largest connected components (LCC).
This analysis allowed us to conclude that, among the three compared databases, ACSN is characterized by the densest representation of crosstalk and feedback regulations between molecular mechanisms. We also compared the set of publications used to annotate the three pathway databases. The overlap of ACSN with REACTOME and NCI PID is as small as 10%. ACSN contains relatively more papers published after 2010 than REACTOME and NCI PID (NCI PID was not updated since 2012). The median date of an annotated reference in ACSN is 4 years younger compared with REACTOME and NCI PID (Supplementary Figures  S8A  and B ). ACSN uses reviews (unlike PID which almost never uses review publications) and journals specific to cancer (such as Oncogene and Cancer Research) more frequently and have a relatively broader journal choice than the other pathway databases.
For example, 22% of publications used to annotate NCI PID are from Journal of Biological Chemistry, while ACSN uses this journal in 13% of annotating references, exploiting other journals more broadly (Supplementary Figure  S8C ). In general, we concluded that the content of ACSN is not redundant with the two other pathway databases compared, though the most canonical molecular pathways are represented similarly in all of them. To further quantify the cancer-related specificity of ACSN and its degree of completeness, the coverage of various census lists of cancer-related genes by ACSN was computed. ACSN covers 60% (87 out of 138) of the cancer-driver genes from Vogelstein et al. 41  and 31% (150 out of 488) of the more exploratory Catalogue Of Somatic Mutations In Cancer (COSMIC) census gene list,  42  demonstrating that ACSN includes a significant number of cancer-related processes for which a sufficient mechanistic understanding exists.
The ACSN maps can be used to visualize and interpret the global trends in gene expression data as well as the phosphoproteomic data that are able to provide more relevant information than the gene expression data alone. 44 Interestingly, users can also visualize the results of any statistical analyses applied to a data set. For instance, a matrix of P-values resulting from a differential gene expression analysis between two conditions (for example, cancer/ normal) can be visualized by map staining. In addition, users can perform a functional enrichment of ACSN modules statistical analysis from a gene list (for example, a list of differentially expressed genes) directly in the ACSN environment (Supplementary Figure  S9 ). The data visualization and analysis functionality is illustrated in several case studies using cancer data: in the live example of visualization of The Cancer Genome Atlas (TCGA) ovarian cancer data set, in the tutorial, and in the guide available from https://acsn.
To illustrate applicability of ACSN for visualizing mutation data, we mapped the most frequently mutated oncogenes and tumour-suppressor genes (TSGs) in breast and lung subsets from TCGA data  41  on ACSN (Figure  4  and Table  3 ). For both types of cancer, TSG mutations are more frequently found in cell cycle and DNA repairrelated modules of ACSN than oncogenes. This might indicate that most probably TSGs in those processes normally contribute to the restriction of uncontrolled divisions and unrepaired DNA but are inactivated by mutations in cancer. Cell survival, EMT and cell motility and cell death related-processes are affected by mutations in both classes of genes in two cancers (Supplementary Table  S4 ). As expected, the coverage of different biological processes by mutated oncogenes and TSGs is not uniform in the two cancer types.
39 CSN can be used for visualization of both the individual gene expression levels and of the functional module activity level. In our example, the activity of a module is computed by averaging the expression values of participating genes (see Materials and methods). The territory 'occupied' by a module on the ACSN map is highlighted using a colour gradient denoting the module 'activity' (see Materials and methods). This 'map staining' data visualization technique can be combined with other types of data visualization. To illustrate this approach, mRNA expression data of four BC subtypes from TCGA database were visualized using ACSN. As shown in the Figure  5 , the global patterns of ACSN module activities clearly vary between the four BC subtypes. According to the consensus molecular characteristics,  62  Basal-like tumours are highly mitotic, overexpressing DNA repair-related and phosphoinositide-3 kinase/AKT pathways.
Consistently with this, Cell cycle, DNA repair modules and Survival and Apoptosis modules are overactivated in ACSN, (Figure  5a ). A practically opposite picture of the activity of ACSN modules is seen in the Luminal A type (Figure  5d ); whereas HER2-positive and Luminal B subtypes lie between those two most distinct BC subtypes (Figures  5b  and c ) that is also consistent with the previous studies. We also used ACSN to visualize the consensus molecular signatures of BC subtypes. 62 It is notable that a number of upregulated genes from the Basal-like molecular signature are also involved in functional modules related to the EMT regulators, Cell-Cell adhesions, Cell-Matrix adhesions and Cytoskeletal polarity (Supplementary Figure  S10 ). The activities of the modules EMT regulators and cell-cell adhesions are higher in Basal-like subtype (Figure  5a ) compared with Luminal A subtype (Figure  5d ).
65 In addition, we visualized mRNA expression levels of each gene represented on ACSN for four BC subtypes simultaneously, allowing, for example, to grasp differences in regulation of genes involved in the same complex (Figure  5e ) or differences in expression across members of the gene family (Figure  5f ) for four subtypes of BC. Thus various modes of highthroughput data visualization in the context of ACSN help to follow expression changes in individual genes and also to understand the biological functional significance of more general gene expression trends at the level of module activity. Visualization of phosphoproteomic data for lung cancer tumour samples. Although gene expression measurements in tumour samples are the most abundant source of genome-wide data in cancer research, dysfunction of signalling pathways often originates from abnormal regulation of posttranslational modifications in cancer.
Recently, qualitative and quantitative data on the posttranslational modifications, especially the phosphorylation status of the key proteins involved in cancer-related signalling, started to be generated at a relatively large scale. 66 nlike many existing pathway databases, the representation of molecular events in ACSN includes posttranslational modifications of proteins. When available, ACSN contains information on specific amino-acid modifications. For example, ACSN covers information about 500 phosphorylated protein modifications in addition to other posttranslational modifications. This granularity of representation in ACSN opens the possibility of visualizing protein posttranslational modification data. We mapped the frequencies of phosphorylated proteins in lung cancer obtained from the PhosphoSitePlus database  43  and identified several functional modules on ACSN map where the local density of frequently phosphorylated protein forms was particularly high (Figure  6 ).
Expectedly, the MAPK module is characterized by phosphorylation of classical kinases such as mitogen-activated protein kinase kinase, extracellular signalregulated kinase, AKT and glycogen synthase kinase 3 beta and their targets (Figure  6a ). The role of phosphorylation in the regulation of cell-matrix interactions is a less studied area. Data visualization also sheds light on how phosphorylation events are involved in lung cancer. It is known that phosphorylation of PXN and PKT2 contributes to reorganization of focal adhesions and cell motility in lung cancer. 67 The interaction between PXN and PTK2 proteins, following their phosphorylation, found in Cell-Matrix adhesion module might be a sign of for increased cell motility potential in the studied lung cancer subset (Figure  6b ). Comparison of high-throughput data for visualization and analysis using ACSN and other databases.
We have compared the data visualization capabilities in ACSN with other pathway databases (Supplementary Table  S1 ). To demonstrate the different modes of data visualization, we have chosen the maps corresponding to cell cycle processes as the most characterized and detailed cell process found in the majority of pathway databases, including KEGG and REACTOME. As shown in Supplementary Figure  S11 , ACSN provides a unique possibility to visualize numerous and heterogeneous data types simultaneously, the feature that does not exist in KEGG and REACTOME databases. The data integrated into ACSN maps can be observed at different levels of zoom, from the top level view, where the general pattern of data distribution can be observed, to the most detailed level, where the data associated with individual molecular entities can be studied.
68, 69 Such quantitative rather than qualitative network changes compared with normal cell signalling could be studied in the context of comprehensive signalling networks by analysing experimental data obtained from cancer samples, cancer-related cell lines or animal models. With this aim, we have created ACSN, a web-based resource and environment for systems level study and data analysis in cancer research. It combines a global signalling map representing molecular processes implicated in cancer, a web-based system for navigation, querying and commenting the map and the builtin tools for data visualization and analysis in the context of the signalling map. It is important to underline that ACSN is not a compilation of the information extracted from already existing pathway databases. Although ACSN map managers consult external databases, its content is based on manual literature mining and is, therefore, completely original.
This paradigm is different from the existing efforts on communitybased pathway database creation such as Payao  70  or WikiPathways. 71 ACSN updates are controlled by the map The comprehensive maps of molecular interactions may serve for multilevel data integration,  72  exploiting the concepts used for integrating data on top of geographical maps. Visualizing different data types, from simple gene lists to phosphoproteomic data, is made possible due to the nature of ACSN pathway representation in the form of a global and detailed biochemical reaction network,  In summary, ACSN is a tool that supports transfer of knowledge on molecular mechanisms altered in cancer to the computational methods for diagnosis, prediction of drug resistance, patient stratification and search for synthetic interactions that will help to design combinatorial therapeutic strategies.
ACSN documentation, data and code availability An introduction into the features of ACSN, video tutorial, downloadable user guide and documentation about ACSN database are available on the ACSN web page (http://acsn. curie. fr). The ACSN maps are freely available for downloading from the ACSN web page (https://acsn. curie. fr/down loads. html) in BioPAX and PNG formats. The module composition of ACSN is available as GMT files. The description of visualization and analysis functionality, including case studies, tutorial, guide and online live examples, are available from https://acsn. curie. fr/documentation. html. The Java code used to assemble the ACSN map and convert it into the NaviCell format is a part of BiNoM Cytoscape plugin and available at http:// binom. curie. fr.
Graphical standards and data model The knowledge is formalized using Process Description language from the Systems Biology Graphical Notation standard 7 implemented in the CellDesigner software  8  (Supplementary Figure  S1 ). ACSN distinguishes the following molecular entities: protein, gene, RNA, antisense RNA, simple molecule, ion, drug, phenotype, complexes. Cellular compartments such as cytoplasm, nucleus, mitochondria, and so on recapitulate the cellular architecture. ACSN is a biochemical reaction network formed by reactants, products and various types of regulators. Edges on the map represent biochemical reactions or reaction regulations of various types, including posttranslational modifications, translation, transcription, complex formation or dissociation, transport, degradation and so on.
ACSN content: literature and database mining and data input The map curator studies the body of literature dedicated to the biological process or molecular mechanism of interest. The initial sources of information are the major review articles from high-impact journals that represent the consensus view on the studied topic and also provide a list of original references. The map curator extracts information from review papers and represents it in the form of biochemical reactions in CellDesigner. This level of details reflects the 'canonical' mechanisms. Afterwards, the curator extends the search and analyses original papers from the list provided in the review articles and beyond. This information is used to enrich the map with details from the recent discoveries in the field. The rule for confident acceptance and inclusion of a biochemical reaction or a process is the presence of sufficient evidences from more than two studies, preferably from different scientific groups.
ACSN annotation format Each entity and reaction on the map is annotated using the NaviCell annotation format that includes sections 'Identifiers', 'Maps_Modules' and 'References'. 'Identifiers' section provides links to the corresponding entity descriptions in HGNC, UniProt, Entrez, GeneCards, REACTOME, Kegg and WikiPathways databases. 'Maps_Modules' section includes links to maps and modules of ACSN. 'References' section includes notes added by the map manager and links to relevant publications. Annotations can be associated with a molecular entity (such as protein) as well as to its particular modification (such as a particular posttranslational modification) (Supplementary Figure  S3 ). Each entity on ACSN is also cross-referenced with databases as KEGG PATHWAY,  38  REACTOME,  74  Atlas of Genetics and Cytogenetics in Oncology and Haematology,  75  GeneCards,  76  Wiki Genes 77 and others.
ACSN reaction and protein complex confidence scores To provide information on the reliability of the depicted molecular interactions, two confidence score have been introduced into ACSN. Both scores represent integer numbers varying from 0 (undefined confidence) to 5 (high confidence). The reference score (REF) indicates both the number and the 'weight' associated with publications found in the annotation of a given reaction, with weight equal to 1 point for an original publication and 3 points for a review article. For example, a reaction annotated by two reviews will obtain the highest reference score REF = 5. The functional proximity score (FUNC) is computed based on the external network of protein-protein interactions (PPI), Human Protein Reference Database, which contains both experimental and literature-based curated interaction data.
High-throughput data sources and analysis To illustrate the coverage of gene mutation frequencies over the ACSN maps, the information about mutations in breast and lung cancers was obtained from the COSMIC database. 42 The frequency of a gene mutation is represented as a ratio of samples carrying a mutation over the number of samples tested in each data set. For mRNA expression analysis, normalized RNASeq data for 745 breast tumours and 100 healthy samples were obtained from TCGA database. Raw count data were first normalized using the Upper Quartile method  79  and then grouped into the four BC subtypes:  80  Basal-like (79 samples), Her2-positive (32 samples), Luminal A (385 samples), and Luminal B (118 samples). Each gene was assigned a score using median expression level across samples of the same subgroup, for ACSN map staining.