{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Xu Y"],"funding":["National Natural Science Foundation of China","NCI NIH HHS","National Institutes of Health","Program for Innovative Research Team of Shanghai University of Finance and Economics","Yale Cancer Center Pilot Award","Shanghai Pujiang Program","National Science Foundation"],"pagination":["1542-1554"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9366385"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["78(4)"],"pubmed_abstract":["Multiple types of molecular (genetic, genomic, epigenetic, etc.) measurements, environmental risk factors, and their interactions have been found to contribute to the outcomes and phenotypes of complex diseases. In each of the previous studies, only the interactions between one type of molecular measurement and environmental risk factors have been analyzed. In recent biomedical studies, multidimensional profiling, in which data from multiple types of molecular measurements are collected from the same subjects, is becoming popular. A myriad of recent studies have shown that collectively analyzing multiple types of molecular measurements is not only biologically sensible but also leads to improved estimation and prediction. In this study, we conduct an M-E interaction analysis, with M standing for multidimensional molecular measurements and E standing for environmental risk factors. This can accommodate multiple types of molecular measurements and sufficiently account for their overlapping as well as independent information. Extensive simulation shows that it outperforms several closely related alternatives. In the analysis of TCGA (The Cancer Genome Atlas) data on lung adenocarcinoma and cutaneous melanoma, we make some stable biological findings and achieve stable prediction."],"journal":["Biometrics"],"pubmed_title":["Multidimensional molecular measurements-environment interaction analysis for disease outcomes."],"pmcid":["PMC9366385"],"funding_grant_id":["1916251","R03 CA241699","R03 CA216017","12071273","R01 CA204120","CA204120","CA216017","CA241699","19PJ1403600"],"pubmed_authors":["Wu M","Ma S","Xu Y"],"additional_accession":[]},"is_claimable":false,"name":"Multidimensional molecular measurements-environment interaction analysis for disease outcomes.","description":"Multiple types of molecular (genetic, genomic, epigenetic, etc.) measurements, environmental risk factors, and their interactions have been found to contribute to the outcomes and phenotypes of complex diseases. In each of the previous studies, only the interactions between one type of molecular measurement and environmental risk factors have been analyzed. In recent biomedical studies, multidimensional profiling, in which data from multiple types of molecular measurements are collected from the same subjects, is becoming popular. A myriad of recent studies have shown that collectively analyzing multiple types of molecular measurements is not only biologically sensible but also leads to improved estimation and prediction. In this study, we conduct an M-E interaction analysis, with M standing for multidimensional molecular measurements and E standing for environmental risk factors. This can accommodate multiple types of molecular measurements and sufficiently account for their overlapping as well as independent information. Extensive simulation shows that it outperforms several closely related alternatives. In the analysis of TCGA (The Cancer Genome Atlas) data on lung adenocarcinoma and cutaneous melanoma, we make some stable biological findings and achieve stable prediction.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Dec","modification":"2025-04-04T09:14:13.567Z","creation":"2025-04-04T09:14:13.567Z"},"accession":"S-EPMC9366385","cross_references":{"pubmed":["34213006"],"doi":["10.1111/biom.13526"]}}