Department of Industrial Engineering2024-11-0920191367-480310.1093/bioinformatics/btz4462-s2.0-85077770930http://dx.doi.org/10.1093/bioinformatics/btz446https://hdl.handle.net/20.500.14288/13423Motivation: Survival analysis methods that integrate pathways/gene sets into their learning model could identify molecular mechanisms that determine survival characteristics of patients. Rather than first picking the predictive pathways/gene sets from a given collection and then training a predictive model on the subset of genomic features mapped to these selected pathways/gene sets, we developed a novel machine learning algorithm (Path2Surv) that conjointly performs these two steps using multiple kernel learning. Results: We extensively tested our Path2Surv algorithm on 7655 patients from 20 cancer types using cancer-specific pathway/gene set collections and gene expression profiles of these patients. Path2Surv statistically significantly outperformed survival random forest (RF) on 12 out of 20 datasets and obtained comparable predictive performance against survival support vector machine (SVM) using significantly fewer gene expression features (i.e. less than 10% of what survival RF and survival SVM used).Biochemical research methodsBiotechnologyApplied microbiologyComputer scienceMathematicalComputational biologyStatisticsProbabilityJournal Article1367-4811509361200009Q12082