short-paper

A Flexible and Robust Multi-Source Learning Algorithm for Drug Repositioning

Authors:

Jing LiAuthors Info & Claims

ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

Pages 510 - 515

https://doi.org/10.1145/3107411.3107473

Published: 20 August 2017 Publication History

Abstract

Drug repositioning is a promising strategy in drug discovery. New biomedical insights of drug-target-disease relationships are important in drug repositioning, and such relationships have been intensively studied recently. Most of the studies utilize network-based computational approaches based on drug and disease similarities. However, one common limitation of existing approaches is that both drug similarities and disease similarities are defined based on a single feature of drugs/diseases. In reality, the relationships between drug (or disease) pairs can be characterized based on many different features. Therefore, it is increasingly important to include them in drug repositioning studies. In this study, we propose a flexible and robust multi-source learning (FRMSL) framework to integrate multiple heterogeneous data sources for drug-disease association predictions. We first construct a two-layer heterogeneous network consisting of drug nodes, disease nodes and known drug-disease relationships. The drug repositioning problem can thus be treated as a missing link prediction problem on the heterogeneous graph and can be solved using Kronecker regularized least square (KronRLS) method. Multiple data sources describing drugs and diseases are incorporated into the framework using similarity-based kernels. In practice, a great challenge in such data integration projects is the data incompleteness problem due to the nature of data generation and collection. To address this issue, we develop a novel multi-view learning algorithm based on symmetric nonnegative matrix factorization (SymNMF). Extensive experimental studies show that our framework outperforms several recent network-based methods.

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Cited By

Gu YZheng SZhang BKang HJiang RLi J(2025)Deep multiple instance learning on heterogeneous graph for drug–disease association predictionComputers in Biology and Medicine10.1016/j.compbiomed.2024.109403184(109403)Online publication date: Jan-2025
https://doi.org/10.1016/j.compbiomed.2024.109403
Qiu XWang HTan XFang Z(2024)G-K BertDTAComputers in Biology and Medicine10.1016/j.compbiomed.2024.108376173:COnline publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.108376
Wang SLi JWang DXu DJin JWang Y(2023)Predicting Drug-Disease Associations Through Similarity Network Fusion and Multi-View Feature Projection RepresentationIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2023.330071727:10(5165-5176)Online publication date: Oct-2023
https://doi.org/10.1109/JBHI.2023.3300717
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Index Terms

A Flexible and Robust Multi-Source Learning Algorithm for Drug Repositioning
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches

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cover image ACM Conferences

ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

August 2017

800 pages

ISBN:9781450347228

DOI:10.1145/3107411

General Chairs:
Nurit Haspel
University of Massachusetts Boston, USA
,
Lenore J. Cowen
Tufts University, USA
,
Program Chairs:
Amarda Shehu
George Mason University, USA
,
Tamer Kahveci
University of Florida, USA
,
Giuseppe Pozzi
Politecnico di Milano, Italy

Copyright © 2017 ACM.

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Published: 20 August 2017

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BCB '17

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SIGBio

BCB '17: 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

August 20 - 23, 2017

Massachusetts, Boston, USA

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ACM-BCB '17 Paper Acceptance Rate 42 of 132 submissions, 32%;

Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

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https://doi.org/10.1016/j.compbiomed.2024.109403
Qiu XWang HTan XFang Z(2024)G-K BertDTAComputers in Biology and Medicine10.1016/j.compbiomed.2024.108376173:COnline publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.108376
Wang SLi JWang DXu DJin JWang Y(2023)Predicting Drug-Disease Associations Through Similarity Network Fusion and Multi-View Feature Projection RepresentationIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2023.330071727:10(5165-5176)Online publication date: Oct-2023
https://doi.org/10.1109/JBHI.2023.3300717
Peng LZhou LChen XPiao X(2020)A Computational Study of Potential miRNA-Disease Association Inference Based on Ensemble Learning and Kernel Ridge RegressionFrontiers in Bioengineering and Biotechnology10.3389/fbioe.2020.000408Online publication date: 6-Feb-2020
https://doi.org/10.3389/fbioe.2020.00040
Shi WChen XDeng L(2020)A Review of Recent Developments and Progress in Computational Drug RepositioningCurrent Pharmaceutical Design10.2174/138161282666620011614555926:26(3059-3068)Online publication date: 11-Aug-2020
https://doi.org/10.2174/1381612826666200116145559
Chen HCheng FLi J(2020)iDrug: Integration of drug repositioning and drug-target prediction via cross-network embeddingPLOS Computational Biology10.1371/journal.pcbi.100804016:7(e1008040)Online publication date: 15-Jul-2020
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Chen HLi J(2019)Modeling Relational Drug-Target-Disease Interactions via Tensor Factorization with Multiple Web SourcesThe World Wide Web Conference10.1145/3308558.3313476(218-227)Online publication date: 13-May-2019
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Chen HIyengar SLi JShi XBuck MMa JVeltri P(2019)Large-scale Analysis of Drug Combinations by Integrating Multiple Heterogeneous Information NetworksProceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3307339.3342142(67-76)Online publication date: 4-Sep-2019
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Chen HLi J(2019)Collective Tensor Completion with Multiple Heterogeneous Side Information2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9006072(731-740)Online publication date: Dec-2019
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