dataesr/scientific-paragraphs-categorization huggingface dataset:
To the best of our knowledge, patched networks were not analysed sufficiently for the detection of retinal fluids. Hence, the models were trained with images containing at least one diseased pixel and thus, led to better performance when trained using the Cirrus and Spectralis dataset. Additionally, trained models using only the diseased images in the Topcon dataset did not improve the performance. The benefit of this method of training is to reduce the training time as well as avoid fitting redundant images that only have background pixels. Lastly, the images and patches were denoised to improve the performance, although the enhancement was not shown to be significant. Nevertheless, the outcomes of these training models are thought to help doctors identify the location of AMD fluids. The use of different weight balancing techniques was also employed to validate the best algorithm.
The median frequency balancing method led to better performance as compared to the other techniques used in this study. Subsequently, the power of fine-tuning was also demonstrated in this study. In the fine-tuning stage, nine volumes were deemed sufficient to perform the network training as opposed to the network that was trained from scratch  (16 volumes) , thus reducing the time required for the training. Furthermore, the network also successfully identified the fluids from different vendors. The experimental results of the deep neural networks using OPTIMA datasets outperformed the performance  [15]  shown in Table  10. However, no comparison was made with other competitors from the OPTIMA challenge as all the teams developed machine learning techniques. Nevertheless, the fine-tuning process of the network facilitates the development of a more solid system, as evidenced in Experiment 5 and Experiment 7.
As indicated by the RETOUCH challenge, the average performance by the human eye was relatively low (DSC = 0. 73), thus warranting the need for determining an accurate decision using network models. It was shown that CNN outperformed manual testing by the human eye and may assist the ophthalmologists in detecting retinal fluids. Nevertheless, all the networks missed the location of the small lesions, although the segmentation of the larger lesions was more feasible. Additionally, the ensembling all the networks together did not boost the performance. The performance of the best network was also performed using the OPTIMA test datasets for further generalisation of networks, whereby it outperformed all the networks proposed by the teams who participated in the RETOUCH challenge. On the other hand, the Nidek dataset from the OPTIMA challenge was the only dataset that was trained from scratch and yielded competitive results.
Network inference From the set of DE genes, we built a gene regulatory network with the combination of CLR  [61]  and BRANE Cut  [40, 62]  inference methods. When the use was judicious, we evaluated our discovered TF-targets interactions by performing a promoter analysis of the plausible targets given by the inferred network, with the Regulatory Sequence Analysis Tool (RSAT)  [63]. More details on the complete methodology for both the inference and the promoter analysis are provided in section Materials and Methods. Network enhancement thresholding performed by BRANE Cut post-processing  [40]  selected 161 genes (including 15 transcription factors) and inferred 205 links (Figure  4 ). In order to help network interpretation, we applied the same color code as for the clustering (Fig. 3 ). We observe a coherence between the function and the expression behavior of genes linked into modules, thus corroborating clustering results.
As we will see in details in the following network analysis, we reveal potential links between three mechanisms grouped in modules (SubN 1 , SubN 2 , and SubN 3 ) and related to cellulase activation, β-glucosidase expression and repression of developmental process. First of all, the global study of the network shows interactions between genes sharing the same gene expression profile. The 161 genes selected by BRANE Cut cover a relatively small number of biological processes, especially regarding half of the 15 retained transcription factors for which only two main biological processes are identified: development (Tr -WET-1, Tr -PRO1, Tr -ACON-3 (IDs 4430, 76590, 123713)) and carbohydrate mechanisms (XYR1, PHM29, ACE3 and CLR2). In addition, we observe a large proportion of genes related to the enzymatic cocktail for cellulase production.
RNA-seq library preparation and analysis Library preparation and RNA-seq data acquisition Library preparation and Illumina sequencing were performed at the École normale supérieure Genomic Platform (Paris, France). Messenger (polyA+) RNAs were purified from 1 µg of total RNA using oligo(dT). Libraries were prepared using the strand specific RNA-seq library preparation TruSeq Stranded mRNA kit (Illumina). Libraries were multiplexed by 6 ˆ6 flowcell lanes. A 50 bp read sequencing was performed on a HiSeq 1500 device (Illumina). An average of 35 ˘10 millions passing Illumina quality filter reads was obtained for each of the 36 samples. The analysis were performed using the Eoulsan pipeline  [79] , including read filtering, mapping, alignment filtering, read quantification, normalisation and differential analysis.
Normalization and differentially expressed genes identification RNA-seq data normalization and differential analysis was performed thanks to the DESeq Bioconductor R package (version 1. 8. 3)  [83]. The normalization method implemented in DESeq assumes that only a few number of genes are differentially expressed and corresponds to a median scale normalization. The differential analysis relies on a statistical model, and more precisely on the negative binomial distribution with variance and mean related by local regression. This approach allows us to identify, for each gene, if the observed difference in read counts is significant. An adjustment for multiple-testing with the procedure of Benjamini and Hochberg  [84]  was also performed. Hence, we assumed that a gene is said differentially expressed when the adjusted p-value was lower than 0.
As a result, the final matrix contains 593 genes, where for each gene the expression profile contains 32 components. This procedure allows us to deal with expression profiles having a sufficient number of components to obtain a more reliable inferred network. Clustering and functional analysis of differentially expressed genes Clustering As previously mentioned, clustering is performed on the 650 genes. Each gene is characterized by its ten-component expression profile. The following approach was completely performed using the Multi Experiment Viewer (MeV) software  [85]. Firstly, a hierarchical clustering allows us to estimate the optimal number K of clusters hidden in the data. By choosing the Euclidean distance metric and the average linkage method, results suggest K " 5 clusters. Then, the K-means algorithm (originating in  [86] ) is preferred in order to obtain a final gene classification.
Promoter analysis The promoter analysis was performed using the Regulatory Sequence Analysis Tools (RSAT) software  [63]. From each set of genes to study (linked to a specific TF), promoter sequences from ´1 to1000 upstream bases are retrieved using the retrieve sequence tool. From these sequences, a detection of over-represented oligonucleotides was performed thanks to the oligo-analysis tool. We used the reference sequence set of Trichoderma reesei as background model. As mentioned in  [89] , this choice is driven by the fact that the input sequences (the query) are a subset of a larger collection (the reference). As a result, we obtain a list of over-expressed oligonucleotides (from hexa-to octo-) and several larger motifs assembled from the previous ones using the pattern assembly tool.
Acknowledgements We would like to thank  Dimitri Ivanoff ,  Sabine Prigent  and  Thiziri Aouam  for technical assistance. This work was supported by the  France Génomique national infrastructure , funded as part of the  "Investissements d'Avenir" program  managed by the  Agence Nationale de la Recherche  (contract  ANR-10-INBS-09 ).
Efforts of systematic collection and knowledge formalization about molecular interactions in a structured and computer-readable format have already been applied to numerous pathway databases. Several databases contain partially Cancers 2020, 12, 990 3 of 26 overlapping collections of signaling diagrams representing different modes of cell death, mitochondria and glucose metabolism in some detail  [13] [14] [15] [16] [17]. However, the limiting factor of this type of signaling representation is the lack of an integrated view of the pathways linking upstream inputs, such as activators of 'death receptor', damaging agents or intracellular stressors to the different regulated cell death modes. As an additional approach for data formalization, protein-protein interaction (PPI) networks are used to depict reciprocal effects between molecular entities.
The map depicts inputs, the initiating factors that activate different cell death mechanisms, the signaling pathways responsible for the choice of distinct lethal subroutines and the final executing mechanisms. These three elements correspond to the three layers of the map. The RCD map also covers related energy, metabolic, and mitochondrial mechanisms. To facilitate navigation and usage, the map is divided into functional modules. This systems biology resource is freely available, allows easy navigation and curation by the community, facilitated by several web-based platforms such as NaviCell  [24] , MINERVA  [25] , and NDEx  [26]. Finally, we demonstrate that the RCD map can be used as a tool for comparison of cell death regulation in several human diseases.
General Characteristics of Regulated Cell Death Map The molecular mechanisms are depicted on the map in the form of a biochemical reaction network using a well-established methodology  [27]. The diagrams are built using the Systems Biology Graphical Notation language (SBGN)  [28]  and drawn using the CellDesigner tool [29] that ensures compatibility of the maps with various tools for network analysis, data integration, and network modelling. To construct the map, manual literature curation was carefully performed in several steps, first retrieving a level of well-established 'consensus' information from major reviews and then adding details from recent original publications (Figure  1  and Figure  S1 ). RCD is a tightly regulated process characterized by numerous cross-talks between molecular mechanisms.
In order to represent these details in an organized and user-friendly manner, we handled this complexity in two ways. First, the map has a hierarchical structure and is constructed around three successive layers (initiation, signaling, execution). The map is also split into metamodules, then into functional modules, and, ultimately, the most detailed level describes biochemical reactions (Figure  2  and Table  1 ). The second strategy to address complexity relies on the introduction of a tagging system for map layers, modules and each entity and reaction that indicate their involvement in different biological processes. This system allows tracing the participation of entities in different processes and helps to retrieve a backbone structure of the network diagram. The components of the maps are annotated in the NaviCell format, including PubMed references, cross-references with other databases, notes by the map manager.
In addition, each molecular player possesses module tags (Figure  2  and Figure  S2 ) and each reaction is assigned with a confidence score (Figure  S3 ). The RCD map summarizes information 1008 proteins, 260 genes, 93 miRNAs and 2020 reactions. It is based on 800 original scientific publications. The map is divided into three meta-modules and 26 separate functional modules (Table  1 ). The principles and procedure for map construction, including the graphical standards, data model, literature curation rules, data input from other databases, the detailed tagging system and confidence scores description are provided in the Materials and Methods section. The components of the maps are annotated in the NaviCell format, including PubMed references, cross-references with other databases, notes by the map manager.
In addition, each molecular player possesses module tags (Figure  2  and Figure  S2 ) and each reaction is assigned with a confidence score (Figure  S3 ). The RCD map summarizes information 1008 proteins, 260 genes, 93 miRNAs and 2020 reactions. It is based on 800 original scientific publications. The map is divided into three meta-modules and 26 separate functional modules (Table  1 ). The principles and procedure for map construction, including the graphical standards, data model, literature curation rules, data input from other databases, the detailed tagging system and confidence scores description are provided in the Materials and Methods section.
Access and Navigation of RCD Map The RCD map can be browsed online at https://navicell. curie. fr/pages/maps_rcd. html. The map is presented in three independent platforms, namely NaviCell, MINERVA  [25]  and NDEx  [26]  that can be accessed from the map home page. The map components are clickable, making it interactive. The extended annotations of the map components contain a rich tagging system converted to links. This allows tracing the involvement of molecules into different map sub-structures as meta-modules and modules. The tagging system also allows using the map as a source of annotated signatures of different cell death modes (Figure  2  and Figure  S2 ). The semantic zooming feature of NaviCell  [48]  simplifies navigation throughout the large collection of molecular interactions, revealing a readable amount of details at each zoom level.
Comparison of RCD Map with Similar Pathway Resources Number of regulated cell death mechanisms are described in several existing pathways resources as KEGG  [14]  and REACTOME  [15]  pathway databases. There are additional cell death-related resources in the field, among others there is DeathBase (http://www. deathbase. org), a database on proteins involved in cell death  [49]  and ApoptoProteomics (http://apoptoproteomics. uio. no), an integrated database for analysis of proteomics data which also contains valuable information matching cell death proteins to anti-cancer drugs  [50]. Finally, there is a specific yeast programmed cell death online platform (http://ycelldeath. com/index. html) that collects various type of information of three types of PCD in yeast, Apoptosis  [51] , Necrosis, and Autophagy.
The cell death-type specific pathway repositories are rare. So far there is only the Autophagy Regulatory Network database that gathers information on protein-protein interactions related to Autophagy process and also connects these components and regulators with previously existing signaling pathways in public resources  [52]. To our knowledge, there are no other databases specific to other cell death processes. Majority of abovementioned resources do not represent cell death processes in a form of process description diagrams. In fact, the closest to the RCD map representation mode and therefore the most comparable databases are KEGG and REACTOME, where pathways are shown using a syntax similar to one applied to generate the RCD map. The RCD map has been compared to the relevant pathways describing cell death-related processes from the KEGG and REACTOME pathway databases.
Altogether these relevant selected pathways cover 956 HUGO names in KEGG and 805 HUGO names in REACTOME. These lists were compared with the RCD map that contains 891 HUGO names (Table  S2 ). This number is different than the one reported in Table  1  as we choose to only select HGNC approved gene symbol to have a reliable comparison with the two other databases. For example, "cleaved caspase1*" and "caspase 1*" count as two different entities in Table  1  but correspond to only one HGNC approved symbol (CASP1) in Table  S2. Some RCD modules represent the processes at a more detailed level in terms of the number of gene names, comparing to the corresponding pathways from KEGG and REACTOME databases. For example, Programmed Cell Death pathway in REACTOME contains 178 HUGO names, this corresponds to the Apoptosis and Necroptosis modules in RCD map, together covering 284 HUGO names.
On the contrary, the necroptosis module is slightly more enriched in the KEGG pathway than in the RCD map (165 in KEGG vs. 154 in ACSN). However, in KEGG, the parthanatos pathway (especially the groups of histone genes) is included within the necroptosis module. In contrary, in the RCD map, parthanatos is described as an independent module and is updated with the most recent publications related to cancer. Some metabolic pathways are more represented in other databases, comparing to the RCD map, as this applies to the pentose phosphate pathway and porphyrin metabolism (that actually includes chlorophyll metabolism) in KEGG, as well as to fatty acid metabolism in REACTOME (Table  S2 ). However, it is important to stress that metabolic reactions are not within the scope of the RCD map unless they modulate cell death-related processes.
The overlap of the RCD map HUGO names with other pathways reaches 47% for KEGG and 36% for REACTOME. 323 out of 805 HUGO names from REACTOME pathways and 423 out of 956 proteins from KEGG pathway are also present in the RCD map (Figure  4A  and Table  S2 ). We also compared the publications used to annotate the RCD map and the corresponding selected pathways in KEGG and REACTOME resources. Of note, 815 papers out of 829 papers that were used to annotate the RCD map are unique and are not referenced in the annotations of selected pathways from KEGG or Reactome (Figure  S5B ). Although the median age of the literature references in the RCD map is older than in the corresponding pathways from the other two databases, 25% of the papers referenced in the RCD map have been published relatively recently, between 2010-2018 (Figure  4B ).
Finally, the journal types represented in the three databases were compared. The range of the journals used for annotation of the RCD map and the other two databases is relatively similar. Remarkably, 375 HUGO names are unique for the RCD map (Figure  4A  and Figure  S5A ). These unique entities are homogeneously distributed across the map. More recently described RCD modes, such as ferroptosis and pyroptosis that are carefully represented in the RCD map are close-to-completely absent from KEGG and REACTOME. Altogether, we conclude that the contents of the RCD map are not redundant with other pathway databases and that a significant proportion of entities are unique to the RCD map. We also compared the publications used to annotate the RCD map and the corresponding selected pathways in KEGG and REACTOME resources.
Of note, 815 papers out of 829 papers that were used to annotate the RCD map are unique and are not referenced in the annotations of selected pathways from KEGG or Reactome (Figure  S5B ). Although the median age of the literature references in the RCD map is older than in the corresponding pathways from the other two databases, 25% of the papers referenced in the RCD map have been published relatively recently, between 2010-2018 (Figure  4B ). Finally, the journal types represented in the three databases were compared. The range of the journals used for annotation of the RCD map and the other two databases is relatively similar. However, the distribution of papers from different types of journals is rather different. In the annotations of the RCD map, the citations to journals in the area of molecular biology are overrepresented, compared to the other two databases.
[55]. The module activity values were plotted on the RCD map using the BiNoM plugin of Cytoscape to visualize the differences between the two diseases  [56]. The ROMA scores of AD and NSCLC exhibited rather an inverse trend (Figure  5  and Figures  S6  and S7 ). Regarding RCD types, the modules corresponding to the TRAIL response, Pyroptosis, and Dependence Receptors were more active in AD (Figure  5A ). In contrast, in NSCLC, the ligand-receptor modules (TNF response, TRAIL response, and FAS response), as well as some modules of the signaling layer (Pyroptosis and Dependence Receptors), are less active (Figure  5A , Figure  S7 ). Of note, most modules corresponding to the pyroptosis module appeared to be more active in Alzheimer disease. In addition, in NSCLC several metabolism-related modules (including glucose metabolism, oxidative phosphorylation and the citrate cycle), as well as ER stress, were more active (Figure  5B, C ).
[55]. The module activity values were plotted on the RCD map using the BiNoM plugin of Cytoscape to visualize the differences between the two diseases  [56]. The ROMA scores of AD and NSCLC exhibited rather an inverse trend (Figure  5  and Figures  S6  and S7 ). Regarding RCD types, the modules corresponding to the TRAIL response, Pyroptosis, and Dependence Receptors were more active in AD (Figure  5A ). In contrast, in NSCLC, the ligandreceptor modules (TNF response, TRAIL response, and FAS response), as well as some modules of the signaling layer (Pyroptosis and Dependence Receptors), are less active (Figure  5A , Figure  S7 ). Of note, most modules corresponding to the pyroptosis module appeared to be more active in Alzheimer disease. In addition, in NSCLC several metabolism-related modules (including glucose metabolism, oxidative phosphorylation and the citrate cycle), as well as ER stress, were more active (Figure  5B, C ).
This metabolism-related modules activity confirm previous studies  [57, 58] , including on the difference between Alzheimer's disease and lung cancer  [55]. Indeed, the integrated comparison of the RCD map across AD and NSCLC is in line with speculations on the inverse comorbidity between both diseases  [59] [60] [61] , as well epidemiological studies suggesting that NSCLC occurs less frequently in AD patients than in age-matched individual without AD  [62]. In the next step, the top contributing genes for each disease were identified. For this, the correlation coefficients in all the studies were calculated and those genes that had a correlation coefficient of minimum 0. 5 (absolute value), with a significant p-value and appeared in at least 2 of  Finally, we performed the enrichment study using the Gene Set Enrichment Analysis (GSEA) to complement the ROMA analysis.
The visualization of the ROMA scores in the context of RCD map revealed important differences among these subcategories (Figure  6 ). The differentiated subtype exhibited several active modules in the area of metabolism (mitochondrial metabolism, glucose metabolism, oxidative phosphorylation and TCA cycle). In contrast, apoptosis-related and starvation/autophagy-related modules appeared to be deactivated (Figure  6A ). As to be expected  [81] , the immunoreactive subtype was characterized by the upregulation of the majority of the modules related to death receptor pathways and caspase-related executors (Figure  6B ). In addition, we observed the activation of the modules initiated by inflammation, such as pyroptosis and-to a lower degree-starvation/autophagy (Figure  6C ). The mesenchymal type, being the most aggressive one, displayed high activity in some death-executing modules, with a notable suppression of oxidative phosphorylation and TCA cycle as well as the antioxidant response (Figure  6B ).
Map Construction The signaling network of RCD regulation was manually constructed and based on information extracted from literature curation retrieved from the PubMed database. The map was composed using the CellDesigner tool  [35]  and the standard visual syntax Systems Biology Graphical Notation (SBGN)  [36]  for representing molecular biology mechanisms. The software creates a structured representation in Systems Biology Markup Language (SBML), suitable for further computational analysis  [37]. Each entity of the map is annotated by corresponding references and specific notes.