TY - JOUR
T1 - Predicting drug synergy for precision medicine using network biology and machine learning
AU - Cuvitoglu, Ali
AU - Zhou, Joseph X.
AU - Huang, Sui
AU - Isik, Zerrin
PY - 2019/4/1
Y1 - 2019/4/1
N2 - Identification of effective drug combinations for patients is an expensive and time-consuming procedure, especially for in vitro experiments. To accelerate the synergistic drug discovery process, we present a new classification model to identify more effective anti-cancer drug pairs using in silico network biology approach. Based on the hypotheses that the drug synergy comes from the collective effects on the biological network, therefore, we developed six network biology features, including overlap and distance of drug perturbation network, that were derived by using individual drug-perturbed transcriptome profiles and the relevant biological network analysis. Using publicly available drug synergy databases and three machine-learning (ML) methods, the model was trained to discriminate the positive (synergistic) and negative (nonsynergistic) drug combinations. The proposed models were evaluated on the test cases to predict the most promising network biology feature, which is the network degree activity, i.e. The synergistic effect between drug pairs is mainly accounted by the complementary signaling pathways or molecular networks from two drugs.
AB - Identification of effective drug combinations for patients is an expensive and time-consuming procedure, especially for in vitro experiments. To accelerate the synergistic drug discovery process, we present a new classification model to identify more effective anti-cancer drug pairs using in silico network biology approach. Based on the hypotheses that the drug synergy comes from the collective effects on the biological network, therefore, we developed six network biology features, including overlap and distance of drug perturbation network, that were derived by using individual drug-perturbed transcriptome profiles and the relevant biological network analysis. Using publicly available drug synergy databases and three machine-learning (ML) methods, the model was trained to discriminate the positive (synergistic) and negative (nonsynergistic) drug combinations. The proposed models were evaluated on the test cases to predict the most promising network biology feature, which is the network degree activity, i.e. The synergistic effect between drug pairs is mainly accounted by the complementary signaling pathways or molecular networks from two drugs.
UR - http://www.scopus.com/inward/record.url?scp=85065168632&partnerID=8YFLogxK
U2 - 10.1142/S0219720019500124
DO - 10.1142/S0219720019500124
M3 - Article
SN - 0219-7200
VL - 17
JO - Journal of Bioinformatics and Computational Biology
JF - Journal of Bioinformatics and Computational Biology
IS - 2
M1 - 1950012
ER -