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Deep Inductive Matrix Completion for Biomedical Interaction Prediction

Wang, Haohan and Wei, Yibing and Cao, Mengxin and Xu, Min and Wu, Wei and Xing, Eric P. (2019) Deep Inductive Matrix Completion for Biomedical Interaction Prediction. In: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE , Piscataway, NJ, pp. 520-527. ISBN 9781728118673.

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In many real tasks, side information in addition to the observed entries is available in the matrix completion problem. To make good use of this information, an inductive approach to matrix completion was proposed where the matrix entries are modeled as a bilinear function of real-valued vectors associated with the rows and the columns. However, it is not effective in handling data of nonlinear structures. In this paper, we propose a novel model called Deep Inductive Matrix Completion (DIMC) for nonlinear inductive matrix completion, which consists of two deep-structure neural networks to extract latent features from high-dimensional known side vectors, and then to predict their relationships using the latent features. In DIMC, the parameters of the neural networks are alternatively optimized to minimize the reconstruction error. Then the missing entries can be readily recovered with the side vectors of rows and columns. We compare DIMC with state-of-the-art methods of linear and nonlinear matrix completion in the tasks of drug repositioning, gene-disease and miRNA-disease association prediction. The experimental results verified that DIMC is capable to provide higher accuracy than existing methods and is applicable to predict inductively on new row-column interactions with auxiliary side information. In addition, we discuss the effects of alternating training frequency on the performance of DIMC and how we can utilize such property to implement a GPU-based parallel computing algorithm that significantly shortens the training time.

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Additional Information:© 2019 IEEE. The authors would like to thank Fen Pei from University of Pittsburgh for early stage discussions. This work is supported by the National Institutes of Health grants R01-GM093156 and P30-DA035778.
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Subject Keywords:matrix completion, drug repositioning, gene-disease association
Record Number:CaltechAUTHORS:20200831-105308677
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Official Citation:H. Wang, Y. Wei, M. Cao, M. Xu, W. Wu and E. P. Xing, "Deep Inductive Matrix Completion for Biomedical Interaction Prediction," 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA, 2019, pp. 520-527, doi: 10.1109/BIBM47256.2019.8983275
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:105166
Deposited By: Tony Diaz
Deposited On:09 Sep 2020 00:03
Last Modified:16 Nov 2021 18:40

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