Enhancing the prediction of disease–gene associations with multimodal deep learning

Author:

Luo Ping1,Li Yuanyuan12,Tian Li-Ping3,Wu Fang-Xiang145

Affiliation:

1. Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, Canada

2. School of Mathematics and Physics, Wuhan Institute of Technology, Wuhan, China

3. School of Information, Beijing Wuzi University, Beijing, China

4. Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, Canada

5. Department of Computer Science, University of Saskatchewan, Saskatoon, Canada

Abstract

Abstract Motivation Computationally predicting disease genes helps scientists optimize the in-depth experimental validation and accelerates the identification of real disease-associated genes. Modern high-throughput technologies have generated a vast amount of omics data, and integrating them is expected to improve the accuracy of computational prediction. As an integrative model, multimodal deep belief net (DBN) can capture cross-modality features from heterogeneous datasets to model a complex system. Studies have shown its power in image classification and tumor subtype prediction. However, multimodal DBN has not been used in predicting disease–gene associations. Results In this study, we propose a method to predict disease–gene associations by multimodal DBN (dgMDL). Specifically, latent representations of protein-protein interaction networks and gene ontology terms are first learned by two DBNs independently. Then, a joint DBN is used to learn cross-modality representations from the two sub-models by taking the concatenation of their obtained latent representations as the multimodal input. Finally, disease–gene associations are predicted with the learned cross-modality representations. The proposed method is compared with two state-of-the-art algorithms in terms of 5-fold cross-validation on a set of curated disease–gene associations. dgMDL achieves an AUC of 0.969 which is superior to the competing algorithms. Further analysis of the top-10 unknown disease–gene pairs also demonstrates the ability of dgMDL in predicting new disease–gene associations. Availability and implementation Prediction results and a reference implementation of dgMDL in Python is available on https://github.com/luoping1004/dgMDL. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

Natural Science and Engineering Research Council of Canada

NSERC

China Scholarship Council

National Natural Science Foundation of China

Science Foundation of Wuhan Institute of Technology

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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