DcDiRNeSa, Drug Combination Prediction by Integrating Dimension Reduction and Negative Sampling Techniques

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Tabatabaei Mina; Rahmani Hossein; Nasiri Motahareh

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Journal Paper

Paper Information

Journal: JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING Year:2023 | Volume:11 | Issue:3 Page(s): 417-427

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Information Journal Paper

Title

DcDiRNeSa, Drug Combination Prediction by Integrating Dimension Reduction and Negative Sampling Techniques

Author(s)

Tabatabaei Mina | Rahmani Hossein | Nasiri Motahareh | Issue Writer Certificate

Keywords

Drug Combination Prediction

Synergistic effect

Computational techniques

deep learning

Abstract

The search for effective treatments for complex diseases, while minimizing toxicity and side effects, has become crucial. However, identifying synergistic combinations of drugs is often a time-consuming and expensive process, relying on trial and error due to the vast search space involved. Addressing this issue, we present a deep learning framework in this study. Our framework utilizes a diverse set of features, including chemical structure, biomedical literature embedding, and biological network interaction data, to predict potential synergistic combinations. Additionally, we employ autoencoders and principal component analysis (PCA) for dimension reduction in sparse data. Through 10-fold cross-validation, we achieved an impressive 98 percent area under the curve (AUC), surpassing the performance of seven previous state-of-the-art approaches by an average of 8%.

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