{"doi":"10.1093/bioinformatics/bty076","title":"Multiobjective multifactor dimensionality reduction to detect SNP–SNP interactions","abstract":"<jats:title>Abstract</jats:title>\n               <jats:sec>\n                  <jats:title>Motivation</jats:title>\n                  <jats:p>Single-nucleotide polymorphism (SNP)–SNP interactions (SSIs) are popular markers for understanding disease susceptibility. Multifactor dimensionality reduction (MDR) can successfully detect considerable SSIs. Currently, MDR-based methods mainly adopt a single-objective function (a single measure based on contingency tables) to detect SSIs. However, generally, a single-measure function might not yield favorable results due to potential model preferences and disease complexities.</jats:p>\n               </jats:sec>\n               <jats:sec>\n                  <jats:title>Approach</jats:title>\n                  <jats:p>This study proposes a multiobjective MDR (MOMDR) method that is based on a contingency table of MDR as an objective function. MOMDR considers the incorporated measures, including correct classification and likelihood rates, to detect SSIs and adopts set theory to predict the most favorable SSIs with cross-validation consistency. MOMDR enables simultaneously using multiple measures to determine potential SSIs.</jats:p>\n               </jats:sec>\n               <jats:sec>\n                  <jats:title>Results</jats:title>\n                  <jats:p>Three simulation studies were conducted to compare the detection success rates of MOMDR and single-objective MDR (SOMDR), revealing that MOMDR had higher detection success rates than SOMDR. Furthermore, the Wellcome Trust Case Control Consortium dataset was analyzed by MOMDR to detect SSIs associated with coronary artery disease.</jats:p>\n                  <jats:p>Availability and implementation: MOMDR is freely available at https://goo.gl/M8dpDg</jats:p>\n               </jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information</jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.</jats:p>\n               </jats:sec>","journal":"Bioinformatics","year":2018,"id":651042,"datarank":0.5456379239589579,"base_score":3.6375861597263857,"endowment":3.6375861597263857,"self_citation_contribution":0.5456379239589579,"citation_network_contribution":0.0,"self_endowment_contribution":0.5456379239589579,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":37,"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":115773,"name":"Li-Yeh Chuang","orcid":null,"position":1,"is_corresponding":false},{"id":1506559,"name":"Yu-Da Lin","orcid":null,"position":2,"is_corresponding":false},{"id":115778,"name":"Cheng-Hong Yang","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Multiobjective multifactor dimensionality reduction to detect SNP–SNP interactions","abstract":"<jats:title>Abstract</jats:title>\n               <jats:sec>\n                  <jats:title>Motivation</jats:title>\n                  <jats:p>Single-nucleotide polymorphism (SNP)–SNP interactions (SSIs) are popular markers for understanding disease susceptibility. Multifactor dimensionality reduction (MDR) can successfully detect considerable SSIs. Currently, MDR-based methods mainly adopt a single-objective function (a single measure based on contingency tables) to detect SSIs. However, generally, a single-measure function might not yield favorable results due to potential model preferences and disease complexities.</jats:p>\n               </jats:sec>\n               <jats:sec>\n                  <jats:title>Approach</jats:title>\n                  <jats:p>This study proposes a multiobjective MDR (MOMDR) method that is based on a contingency table of MDR as an objective function. MOMDR considers the incorporated measures, including correct classification and likelihood rates, to detect SSIs and adopts set theory to predict the most favorable SSIs with cross-validation consistency. MOMDR enables simultaneously using multiple measures to determine potential SSIs.</jats:p>\n               </jats:sec>\n               <jats:sec>\n                  <jats:title>Results</jats:title>\n                  <jats:p>Three simulation studies were conducted to compare the detection success rates of MOMDR and single-objective MDR (SOMDR), revealing that MOMDR had higher detection success rates than SOMDR. Furthermore, the Wellcome Trust Case Control Consortium dataset was analyzed by MOMDR to detect SSIs associated with coronary artery disease.</jats:p>\n                  <jats:p>Availability and implementation: MOMDR is freely available at https://goo.gl/M8dpDg</jats:p>\n               </jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information</jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.</jats:p>\n               </jats:sec>","is_dataset_classified":null,"base_score":3.6375861597263857,"endowment":3.6375861597263857,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"29471406","pmcid":null,"openalex_id":"https://openalex.org/W2790029186","authors":[],"funders":[{"funder_name":"Ministry of Science and Technology","grant_id":"105-2221-E-151 -053 -MY2","title":null},{"funder_name":"Ministry of Science and Technology","grant_id":"106-2811-E-151-002-","title":null}],"total_grants":2,"fwci":3.9687,"citation_percentile":0.93994815,"influential_citations":0,"citation_trend":[{"year":2019,"count":8},{"year":2020,"count":9},{"year":2021,"count":6},{"year":2022,"count":6},{"year":2023,"count":3},{"year":2024,"count":3},{"year":2025,"count":2}],"oa_status":"bronze","license":"https://academic.oup.com/journals/pages/about_us/legal/notices","oa_locations":[{"url":"https://academic.oup.com/bioinformatics/article-pdf/34/13/2228/25098533/bty076.pdf","host_type":"journal"},{"url":"https://academic.oup.com/bioinformatics/article-pdf/34/13/2228/25098533/bty076.pdf","host_type":"publisher"},{"url":"https://academic.oup.com/bioinformatics/article-pdf/34/13/2228/50315714/bioinformatics_34_13_2228.pdf","host_type":"publisher"},{"url":"https://doi.org/10.1093/bioinformatics/bty076","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/29471406","host_type":"repository"}],"fields_of_study":["Genetic Associations and Epidemiology","Bioinformatics and Genomic Networks","Gene expression and cancer classification","Case-Control Studies","Coronary Artery Disease","Epistasis, Genetic","Genetic Predisposition to Disease","Humans","Models, Genetic","Multifactor Dimensionality Reduction","Polymorphism, Single Nucleotide"],"mesh_terms":["Coronary Artery Disease","Epistasis, Genetic","Humans","Models, Genetic","Case-Control Studies","Genetic Predisposition to Disease","Polymorphism, Single Nucleotide","Multifactor Dimensionality Reduction"],"keywords":["Multifactor dimensionality reduction","SNP","Dimensionality reduction","Computer science","Curse of dimensionality","Reduction (mathematics)","Computational biology","Artificial intelligence","Genetics","Biology","Single-nucleotide polymorphism","Genotype","Mathematics"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Partnerships for the goals"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-10T06:33:11.139851Z","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":[]}