{"doi":"10.1101/2022.11.20.517258","title":"Computational Pipeline to Identify Gene signatures that Define Cancer Subtypes","abstract":"<jats:title>Abstract</jats:title>\n                <jats:sec>\n                  <jats:title>Motivation</jats:title>\n                  <jats:p>The heterogeneous nature of cancers with multiple subtypes makes them challenging to treat. However, multi-omics data can be used to identify new therapeutic targets and we established a computational strategy to improve data mining.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Results</jats:title>\n                  <jats:p>Using our approach we identified genes and pathways specific to cancer subtypes that can serve as biomarkers and therapeutic targets. Using a TCGA breast cancer dataset we applied the ExtraTreesClassifier dimensionality reduction along with logistic regression to select a subset of genes for model training. Applying hyperparameter tuning, increased the model accuracy up to 92%. Finally, we identified 20 significant genes using differential expression. These targetable genes are associated with various cellular processes that impact cancer progression. We then applied our approach to a glioma dataset and again identified subtype specific targetable genes.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusion</jats:title>\n                  <jats:p>Our research indicates a broader applicability of our strategy to identify specific cancer subtypes and targetable pathways for various cancers.</jats:p>\n                </jats:sec>","journal":null,"year":null,"id":640150,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"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":1663627,"name":"Vatsal Parikh","orcid":"0000-0002-2413-5423","position":1,"is_corresponding":false},{"id":1179226,"name":"Raphael Kirchgaessner","orcid":"0000-0002-2937-6904","position":2,"is_corresponding":false},{"id":932102,"name":"Ekansh Mittal","orcid":"0000-0001-9034-033X","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Computational Pipeline to Identify Gene signatures that Define Cancer Subtypes","abstract":"<jats:title>Abstract</jats:title>\n                <jats:sec>\n                  <jats:title>Motivation</jats:title>\n                  <jats:p>The heterogeneous nature of cancers with multiple subtypes makes them challenging to treat. However, multi-omics data can be used to identify new therapeutic targets and we established a computational strategy to improve data mining.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Results</jats:title>\n                  <jats:p>Using our approach we identified genes and pathways specific to cancer subtypes that can serve as biomarkers and therapeutic targets. Using a TCGA breast cancer dataset we applied the ExtraTreesClassifier dimensionality reduction along with logistic regression to select a subset of genes for model training. Applying hyperparameter tuning, increased the model accuracy up to 92%. Finally, we identified 20 significant genes using differential expression. These targetable genes are associated with various cellular processes that impact cancer progression. We then applied our approach to a glioma dataset and again identified subtype specific targetable genes.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusion</jats:title>\n                  <jats:p>Our research indicates a broader applicability of our strategy to identify specific cancer subtypes and targetable pathways for various cancers.</jats:p>\n                </jats:sec>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19767382","pmcid":null,"openalex_id":"https://openalex.org/W4309642999","authors":[],"funders":[],"total_grants":0,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"green","license":null,"oa_locations":[{"url":"https://www.biorxiv.org/content/biorxiv/early/2022/11/22/2022.11.20.517258.full.pdf","host_type":"repository"},{"url":"https://www.biorxiv.org/content/biorxiv/early/2022/11/22/2022.11.20.517258.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/2022.11.20.517258","host_type":"publisher"},{"url":"https://doi.org/10.1101/2022.11.20.517258","host_type":"repository"}],"fields_of_study":["Bioinformatics and Genomic Networks","Gene expression and cancer classification","Ferroptosis and cancer prognosis"],"mesh_terms":[],"keywords":["Computational biology","Gene","Cancer","Logistic regression","Breast cancer","Biology","Bioinformatics","Computer science","Machine learning","Genetics"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-07T07:02:45.522907Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}