A Learning Based Method for the Drug-Drug Interaction Detection
Keywords:
Drug-drug interactions, Heterogeneous network, Drug similarity, CNN, Support vector machineAbstract
Drug-drug interactions (DDIs) cause grave concern for those patients who require multiple drugs, and in turn for their doctors, caregivers, and society too. Any detection and knowledge imbibed through such interactions utilizing machine learning enables the pharma industry to do away with certain testing modes and helps physicians to impart optimum care while avoiding severe reactions. Here, we put forth a model for predicting any novel drug–drug interaction from a created heterogeneous network, blending in varied drug-relevant information such as drug-disease correlations and drug-side effect correlations, drug–drug interactions etc. which first runs a network diffusion algorithm on each network to determine the "diffusion state," such as random walk with restart. This absorbs its topological relation to other nodes within this diverse network, and forms a drug vector, which is followed by a Denoising Autoencoder model for reducing vector dimensions and identifying vital features. Then, the convolutional neural network model and Support vector classifier is built for predicting drug interactions and evaluating their performances.
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