{"doi":"10.26502/jbsb.5107019","title":"Machine Learning and Systems Biology Approaches to Characterize Dosage-Based Gene Dependencies in Cancer Cells","abstract":"Mapping of cancer survivability factors allows for the identification of novel biological insights for drug targeting. Using genomic editing techniques, gene dependencies can be extracted in a high-throughput and quantitative manner. Dependencies have been predicted using machine learning techniques on -omics data, but the biological consequences of dependency predictor pairs has not been explored. In this work we devised a framework to explore gene dependency using an ensemble of machine learning methods, and our learned models captured meaningful biological information beyond just gene dependency prediction. We show that dosage-based dependent predictors (DDPs) primarily belonged to transcriptional regulation ontologies. We also found that anti-sense RNAs and long- noncoding RNA transcripts display DDPs. Network analyses revealed that SOX10, HLA-J, and ZEB2 act as a triad of network hubs in the dependent-predictor network. Collectively, we demonstrate the powerful combination of machine learning and systems biology approach can illuminate new insights in understanding gene dependency and guide novel targeting avenues.","journal":"Journal of Bioinformatics and Systems Biology","year":2021,"id":210637,"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":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9536,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":721944,"name":"Choong Yong Ung","orcid":"0000-0002-9876-3473","position":1,"is_corresponding":false},{"id":572613,"name":"Taylor M. Weiskittel","orcid":"0000-0003-3682-0628","position":2,"is_corresponding":false},{"id":472024,"name":"Alex Chen","orcid":"0000-0001-7478-0204","position":3,"is_corresponding":false},{"id":225865,"name":"Cheng Zhang","orcid":"0000-0002-3721-8586","position":4,"is_corresponding":false},{"id":463627,"name":"Cristina Correia","orcid":"0000-0002-9464-7555","position":5,"is_corresponding":false},{"id":31912,"name":"Hu Li","orcid":"0000-0001-5957-5472","position":6,"is_corresponding":false},{"id":799622,"name":"Kevin Meng-Lin","orcid":null,"position":0,"is_corresponding":true}],"reference_count":42,"raw_metadata":null,"created_at":"2026-07-18T23:52:12.491146Z","pmid":"33842927","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":[]}