A0512
Title: A novel computational methodology for clinical characteristic predictive gene network estimation
Authors: Heewon Park - Sungshin Women\'s University (Japan) [presenting]
Abstract: We propose a novel computational methodology for clinical characteristic (e.g., drug sensitivity of cell lines) predictive gene network estimation, called a PredictiveNetwork. The objective function of the PredictiveNetwork consists of loss functions for gene network estimation and prediction, and thus we can estimate gene network and predict clinical characteristic, simultaneously. It implies that the network is estimated to be optimized for not only network estimation but also explain the clinical characteristic, thus we can identify clinical characteristic prediction specific molecular interplays. We extend the PredictiveNetwork to network-based classification and develop a Gene regulatory network-based classifier (GRN-classifier) that estimates the gene network to minimize errors for both network estimation and classification of cell lines, in line with the PredictiveNetwork. The proposed strategies are applied to gastric cancer drugs response predictive network estimation and related marker identification, especially we focus on drug resistance molecular interplays identification. The PredictiveNetwork is applied to gastric cancer drugs response predictive network estimation, and GRN-classifier is applied to classify 5-FU -sensitive/resistant and 5-FU target/non-target cell-lines. Our analysis results suggest that active regulatory system between AKR family is a crucial clue to uncover mechanism of acquired gastric cancer drug resistance.
