Enhancing the prediction of disease-gene associations with multimodal deep learning
Bioinformatics, ISSN: 1460-2059, Vol: 35, Issue: 19, Page: 3735-3742
2019
- 50Citations
- 97Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations50
- Citation Indexes50
- 50
- CrossRef34
- Captures97
- Readers97
- 97
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
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85072705896&origin=inward; http://dx.doi.org/10.1093/bioinformatics/btz155; http://www.ncbi.nlm.nih.gov/pubmed/30825303; https://academic.oup.com/bioinformatics/article/35/19/3735/5368487; https://dx.doi.org/10.1093/bioinformatics/btz155
Oxford University Press (OUP)
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