{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Qin X"],"funding":["National Natural Science Foundation of China","NCI NIH HHS","Shanghai Rising-Star Program","National Institutes of Health","Shanghai Research Center for Data Science and Decision Technology"],"pagination":["1761-1774"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10272285"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["79(3)"],"pubmed_abstract":["Genetic interactions play an important role in the progression of complex diseases, providing explanation of variations in disease phenotype missed by main genetic effects. Comparatively, there are fewer studies on survival time, given its challenging characteristics such as censoring. In recent biomedical research, two-level analysis of both genes and their involved pathways has received much attention and been demonstrated as more effective than single-level analysis. However, such analysis is usually limited to main effects. Pathways are not isolated, and their interactions have also been suggested to have important contributions to the prognosis of complex diseases. In this paper, we develop a novel two-level Bayesian interaction analysis approach for survival data. This approach is th"],"journal":["Biometrics"],"pubmed_title":["Two-level Bayesian interaction analysis for survival data incorporating pathway information."],"pmcid":["PMC10272285"],"funding_grant_id":["P50 CA121974","12071273","R01 CA204120","CA204120","P50 CA196530","CA121974","22QA1403500","CA196530"],"pubmed_authors":["Wu M","Ma S","Qin X"],"additional_accession":[]},"is_claimable":false,"name":"Two-level Bayesian interaction analysis for survival data incorporating pathway information.","description":"Genetic interactions play an important role in the progression of complex diseases, providing explanation of variations in disease phenotype missed by main genetic effects. Comparatively, there are fewer studies on survival time, given its challenging characteristics such as censoring. In recent biomedical research, two-level analysis of both genes and their involved pathways has received much attention and been demonstrated as more effective than single-level analysis. However, such analysis is usually limited to main effects. Pathways are not isolated, and their interactions have also been suggested to have important contributions to the prognosis of complex diseases. In this paper, we develop a novel two-level Bayesian interaction analysis approach for survival data. This approach is th","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Sep","modification":"2025-04-19T01:44:20.711Z","creation":"2025-04-07T12:28:14.869Z"},"accession":"S-EPMC10272285","cross_references":{"pubmed":["36524727"],"doi":["10.1111/biom.13811"]}}