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Synergistic target combination prediction from curated signaling networks: Machine learning meets systems biology and pharmacology.
Chua, Huey Eng; Bhowmick, Sourav S; Tucker-Kellogg, Lisa.
Afiliação
  • Chua HE; School of Computer Science & Engineering, Nanyang Technological University, Singapore; Complexity Institute, Nanyang Technological University, Singapore. Electronic address: hechua@ntu.edu.sg.
  • Bhowmick SS; School of Computer Science & Engineering, Nanyang Technological University, Singapore; Complexity Institute, Nanyang Technological University, Singapore. Electronic address: assourav@ntu.edu.sg.
  • Tucker-Kellogg L; DUKE-NUS Medical School, Singapore. Electronic address: lisa.tucker-kellogg@duke-nus.edu.sg.
Methods ; 129: 60-80, 2017 10 01.
Article em En | MEDLINE | ID: mdl-28552265
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. However, such stochastic modifications ignore the impact of the choice of targets and their activities on the combination's therapeutic effect and off-target effects, which directly affect the solution quality. In this paper, we present mascot, a method that addresses this limitation by leveraging two additional heuristic criteria to minimize off-target effects and achieve synergy for candidate modification. Specifically, off-target effects measure the unintended response of a signaling network to the target combination and is often associated with toxicity. Synergy occurs when a pair of targets exerts effects that are greater than the sum of their individual effects, and is generally a beneficial strategy for maximizing effect while minimizing toxicity. mascot leverages on a machine learning-based target prioritization method which prioritizes potential targets in a given disease-associated network to select more effective targets (better therapeutic effect and/or lower off-target effects); and on Loewe additivity theory from pharmacology which assesses the non-additive effects in a combination drug treatment to select synergistic target activities. Our experimental study on two disease-related signaling networks demonstrates the superiority of mascot in comparison to existing approaches.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Transdução de Sinais / Biologia de Sistemas / Quimioterapia Combinada Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Transdução de Sinais / Biologia de Sistemas / Quimioterapia Combinada Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2017 Tipo de documento: Article