{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Li J"],"funding":["MOE Project of Key Research Institute of Humanities and Social Sciences","National Natural Science Foundation of China"],"pagination":["btaf481"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12452269"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["41(9)"],"pubmed_abstract":["<h4>Motivation</h4>Gene-environment (G-E) interaction analysis is crucial in cancer research, offering insights into how genetic and environmental factors jointly influence cancer outcomes. Most existing G-E interaction methods are regression-based, which may lack flexibility to capture complex data patterns. Recent advances have investigated deep neural network-based G-E models. However, these methods may be more vulnerable to information deficiency due to challenges such as limited sample size and high dimensionality. Apart from genetic and environmental data, pathological images have emerged as a widely accessible and informative resource for cancer modeling, presenting its potential to enhance G-E modeling.<h4>Results</h4>We propose the pathological imaging-assisted neural additive mod"],"journal":["Bioinformatics (Oxford, England)"],"pubmed_title":["GE-IA-NAM: gene-environment interaction analysis via imaging-assisted neural additive model."],"pmcid":["PMC12452269"],"funding_grant_id":["12571313","22JJD910001","72071169","82204153"],"pubmed_authors":["Fang K","Li J","Ma S","Xu Y"],"additional_accession":[]},"is_claimable":false,"name":"GE-IA-NAM: gene-environment interaction analysis via imaging-assisted neural additive model.","description":"<h4>Motivation</h4>Gene-environment (G-E) interaction analysis is crucial in cancer research, offering insights into how genetic and environmental factors jointly influence cancer outcomes. Most existing G-E interaction methods are regression-based, which may lack flexibility to capture complex data patterns. Recent advances have investigated deep neural network-based G-E models. However, these methods may be more vulnerable to information deficiency due to challenges such as limited sample size and high dimensionality. Apart from genetic and environmental data, pathological images have emerged as a widely accessible and informative resource for cancer modeling, presenting its potential to enhance G-E modeling.<h4>Results</h4>We propose the pathological imaging-assisted neural additive mod","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Sep","modification":"2026-06-03T19:15:14.143Z","creation":"2026-04-30T03:10:59.661Z"},"accession":"S-EPMC12452269","cross_references":{"pubmed":["40880282"],"doi":["10.1093/bioinformatics/btaf481"]}}