Improving survival prediction using a novel feature selection and feature reduction framework based on the integration of clinical and molecular data.

Improving survival prediction using a novel feature selection and feature reduction framework based on the integration of clinical and molecular data. Pac Symp Biocomput. 2020;25:415-426 Authors: Neums L, Meier R, Koestler DC, Thompson JA Abstract The accurate prediction of a cancer patient's risk of progression or death can guide clinicians in the selection of treatment and help patients in planning personal affairs. Predictive models based on patient-level data represent a tool for determining risk. Ideally, predictive models will use multiple sources of data (e.g., clinical, demographic, molecular, etc.). However, there are many challenges associated with data integration, such as overfitting and redundant features. In this paper we aim to address those challenges through the development of a novel feature selection and feature reduction framework that can handle correlated data. Our method begins by computing a survival distance score for gene expression, which in combination with a score for clinical independence, results in the selection of highly predictive genes that are non-redundant with clinical features. The survival distance score is a measure of variation of gene expression over time, weighted by the variance of the gene expression over all patients. Selected genes, in combination with clinical data, are used to build a predictive model for survival. We benchmark our approach against commonly used methods, namely lasso- as well as ridge-pena...
Source: Pacific Symposium on Biocomputing - Category: Bioinformatics Tags: Pac Symp Biocomput Source Type: research

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