Predicting drug-drug interactions using multi-modal deep auto-encoders based network embedding and positive-unlabeled learning

Volume: 179, Pages: 37 - 46
Published: Jul 1, 2020
Abstract
Drug-drug interactions (DDIs) are crucial for public health and patient safety, which has aroused widespread concern in academia and industry. The existing computational DDI prediction methods are mainly divided into four categories: literature extraction-based, similarity-based, matrix operations-based and network-based. A number of recent studies have revealed that integrating heterogeneous drug features is of significant importance for...
Paper Details
Title
Predicting drug-drug interactions using multi-modal deep auto-encoders based network embedding and positive-unlabeled learning
Published Date
Jul 1, 2020
Journal
Volume
179
Pages
37 - 46
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