Synergistic target combination prediction from curated signaling networks: Machine learning meets systems biology and pharmacology.

Abstract

Given a signaling network, the target combination prediction problem aims to predict efficacious and safe target combinations for combination therapy. State-of-the-art in silico methods use Monte Carlo simulated annealing (mcsa) to modify a candidate solution stochastically, and use the Metropolis criterion to accept or reject the proposed modifications… (More)
DOI: 10.1016/j.ymeth.2017.05.015

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Cite this paper

@article{Chua2017SynergisticTC, title={Synergistic target combination prediction from curated signaling networks: Machine learning meets systems biology and pharmacology.}, author={Huey Eng Chua and Sourav S. Bhowmick and Lisa Tucker-Kellogg}, journal={Methods}, year={2017}, volume={129}, pages={60-80} }