PlumX Metrics
Embed PlumX Metrics

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

Bioinformatics, ISSN: 1460-2059, Vol: 35, Issue: 19, Page: 3735-3742
2019
  • 50
    Citations
  • 0
    Usage
  • 97
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

Article Description

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.

Bibliographic Details

Provide Feedback

Have ideas for a new metric? Would you like to see something else here?Let us know