NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning
By Xing Chen, B. Ren, Ming Chen, Quanxin Wang, Lixin Zhang, Guiying Yan. 2016
259 citations recorded by the source corpus.
Fungal infection has become one of the leading causes of hospital-acquired infections with high mortality rates. Furthermore, drug resistance is common for fungus-causing diseases. Synergistic drug combinations could provide an effective strategy to overcome drug resistance. Meanwhile, synergistic drug combinations can increase treatment efficacy and decrease drug dosage to avoid toxicity. Therefore, computational prediction of synergistic drug combinations for fungus-causing diseases becomes attractive. In this study, we proposed similar nature of drug combinations: principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa. Furthermore, we developed a novel algorithm termed Network-based Laplacian regularized Least Square Synergistic drug combination prediction (NLLSS) to predict potential synergistic drug combinations by integrating different kinds of information such as known synergistic drug combinations, drug-target interactions, and drug chemical structures. We applied NLLSS to predict antifungal synergistic drug combinations and showed that it achieved excellent performance both in terms of cross validation and independent prediction. Finally, we performed biological experiments for fungal pathogen Candida albicans to confirm 7 out of 13 predicted antifungal synergistic drug combinations. NLLSS provides an efficient strategy to identify potential synergistic antifungal combinations.
Subfields: Computational Theory and Mathematics, Organic Chemistry, Pharmacology
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Citations in the corpus
- Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities (2018, background): “Drug-drug interactions are defined through the concepts of synergy and antagonism [285, 286] and are quantified biologically by measuring the dose-effect curves [287, 288] or cell viability [289, 290, 291, 292, 282, 293, 294].”
- Prediction of synergistic anti-cancer drug combinations based on drug target network and drug induced gene expression profiles (2017, methodology): “The NLLSS platform integrated known ynergistic drug combinations, drug target interactions, and drug hemical structure to predict potentially antifungal synergistic rug combination [7].”