Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction.

ABSTRACT: Methods for phenotype and outcome prediction are largely based on inductive supervised models that use selected biomarkers to make predictions, without explicitly considering the functional relationships between individuals. We introduce a novel network-based approach named Patient-Net (P-Net) in which biomolecular profiles of patients are modeled in a graph-structured space that represents gene expression relationships between patients. Then a kernel-based semi-supervised transductive algorithm is applied to the graph to explore the overall topology of the graph and to predict the phenotype/clinical outcome of patients. Experimental tests involving several publicly available datasets of patients afflicted with pancreatic, breast, colon and colorectal cancer show that our proposed method i

SUBMITTER: Gliozzo J 

PROVIDER: S-EPMC7046773 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature