{"doi":"10.1142/9789813235533_0011","title":"Large-scale analysis of disease pathways in the human interactome","abstract":null,"journal":"Biocomputing 2018","year":2018,"id":609063,"datarank":0.7961738328933723,"base_score":3.4965075614664802,"endowment":3.4965075614664802,"self_citation_contribution":0.5244761342199721,"citation_network_contribution":0.27169769867340016,"self_endowment_contribution":0.5244761342199721,"citer_contribution":0.27169769867340016,"corpus_percentile":null,"corpus_rank":null,"citation_count":32,"citer_count":15,"citers_with_citation_signal":10,"citers_with_endowment":10,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":29867,"name":"Marinka Zitnik","orcid":"0000-0001-8530-7228","position":1,"is_corresponding":false},{"id":16634,"name":"Jure Leskovec","orcid":"0000-0002-5411-923X","position":2,"is_corresponding":false},{"id":1565082,"name":"Monica Agrawal","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Large-scale analysis of disease pathways in the human interactome","abstract":"Discovering disease pathways, which can be defined as sets of proteins associated with a given disease, is an important problem that has the potential to provide clinically actionable insights for disease diagnosis, prognosis, and treatment. Computational methods aid the discovery by relying on protein-protein interaction (PPI) networks. They start with a few known disease-associated proteins and aim to find the rest of the pathway by exploring the PPI network around the known disease proteins. However, the success of such methods has been limited, and failure cases have not been well understood. Here we study the PPI network structure of 519 disease pathways. We find that 90% of pathways do not correspond to single well-connected components in the PPI network. Instead, proteins associated with a single disease tend to form many separate connected components/regions in the network. We then evaluate state-of-the-art disease pathway discovery methods and show that their performance is especially poor on diseases with disconnected pathways. Thus, we conclude that network connectivity structure alone may not be sufficient for disease pathway discovery. However, we show that higher-order network structures, such as small subgraphs of the pathway, provide a promising direction for the development of new methods.","is_dataset_classified":null,"base_score":2.9444389791664403,"endowment":2.9444389791664403,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"29218874","pmcid":"PMC5731453","openalex_id":"https://openalex.org/W2754104053","authors":[],"funders":[{"funder_name":"NIBIB NIH HHS","grant_id":"U54 EB020405","title":null},{"funder_name":"National Science Foundation","grant_id":"1149837","title":"CAREER: Mining structure and dynamics of groups of nodes in real-world networks"}],"total_grants":2,"fwci":8.1048,"citation_percentile":0.9754386,"influential_citations":0,"citation_trend":[{"year":2018,"count":1},{"year":2019,"count":4},{"year":2020,"count":4},{"year":2021,"count":2},{"year":2022,"count":5},{"year":2023,"count":2}],"oa_status":"gold","license":"cc-by-nc","oa_locations":[{"url":"https://doi.org/10.1142/9789813235533_0011","host_type":""},{"url":"https://doi.org/10.1142/9789813235533_0011","host_type":""},{"url":"https://doi.org/10.1101/189787","host_type":"repository"},{"url":"https://pubmed.ncbi.nlm.nih.gov/29218874","host_type":"repository"},{"url":"http://arxiv.org/abs/1712.00843","host_type":"repository"},{"url":"http://export.arxiv.org/pdf/1712.00843","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/5731453","host_type":"repository"},{"url":"https://doi.org/10.48550/arxiv.1712.00843","host_type":"repository"},{"url":"https://www.biorxiv.org/content/biorxiv/early/2017/09/16/189787.full.pdf","host_type":"repository"},{"url":"https://arxiv.org/pdf/1712.00843","host_type":"repository"},{"url":"http://arxiv.org/pdf/1712.00843","host_type":""},{"url":"https://dx.doi.org/10.48550/arxiv.1712.00843","host_type":""},{"url":"http://psb.stanford.edu/psb-online/proceedings/psb18/agrawal.pdf","host_type":""},{"url":"https://dx.doi.org/10.1101/189787","host_type":""},{"url":"https://dx.doi.org/10.1142/9789813235533_0011","host_type":""},{"url":"http://dx.doi.org/10.1101/189787","host_type":""}],"fields_of_study":["Bioinformatics and Genomic Networks","Computational Drug Discovery Methods","Biomedical Text Mining and Ontologies","0301 basic medicine","0206 medical engineering","02 engineering and technology","03 medical and health sciences"],"mesh_terms":["Algorithms","Disease","Humans","Signal Transduction","Computational Biology","Proteome","Protein Interaction Mapping","Proteomics","Protein Interaction Maps"],"keywords":["Interactome","Disease","Computational biology","Drug discovery","Computer science","Biology","Interaction network","Bioinformatics","Genetics","Medicine","Gene","Proteomics","Social and Information Networks (cs.SI)","FOS: Computer and information sciences","Computer Science - Machine Learning","Proteome","Molecular Networks (q-bio.MN)","Computer Science - Social and Information Networks","Machine Learning (cs.LG)","FOS: Biological sciences","Protein Interaction Mapping","Humans","Quantitative Biology - Molecular Networks","Protein Interaction Maps","Algorithms","Signal Transduction"],"sdg_mappings":[{"sdg_number":3,"sdg_label":"3. 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