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
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Abstract 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 is competitive with state-of-the-art supervised and semi-supervised predictive systems. Importantly,P-Net also provides interpretable models that can be easily visualized to gain clues about the relationships between patients, and to formulate hypotheses about their stratification. -
Zhou, Naihui ; Jiang, Yuxiang ; Bergquist, Timothy R. ; Lee, Alexandra J. ; Kacsoh, Balint Z. ; Crocker, Alex W. ; Lewis, Kimberley A. ; Georghiou, George ; Nguyen, Huy N. ; Hamid, Md Nafiz ; et al ( , Genome Biology)