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Neural Collective Matrix Factorization for integrated analysis of heterogeneous biomedical data
Ragunathan Mariappan1, Aishwarya Jayagopal1, Ho Zong Sien1
1Department of Information Systems and Analytics, School of Computing, National University of Singapore, Singapore 117417, Singapore.
Bioinformatics (Oxford, England)
|August 5, 2022
Summary
Neural Collective Matrix Factorization (NCMF) offers a novel neural approach to integrate diverse biomedical data, outperforming existing methods in relation prediction tasks. This advancement enhances representation learning for complex biological datasets.
Area of Science:
- Bioinformatics
- Computational Biology
- Machine Learning
Background:
- Biomedical studies often require integrating data from multiple related sources.
- Collective Matrix Factorization (CMF) models learn from collections of matrices for integrative representations.
- Existing CMF methods struggle with non-linear interactions, varying data sparsity/noise, and multiple data types.
Purpose of the Study:
- To develop a novel, fully neural approach for Collective Matrix Factorization (CMF).
- To address limitations of previous CMF methods in capturing complex interactions and data heterogeneity.
- To improve representation learning for integrating diverse biomedical data.
Main Methods:
- Introduced Neural Collective Matrix Factorization (NCMF), a fully neural CMF model.
- Evaluated NCMF on gene-disease association and adverse drug event prediction tasks.
- Utilized heterogeneous data from publicly available databases for representation learning.
Main Results:
- NCMF demonstrated superior performance compared to previous CMF methods.
- NCMF outperformed state-of-the-art graph embedding methods in representation learning.
- Experiments confirmed NCMF's versatility and efficacy in integrating heterogeneous data.
Conclusions:
- NCMF provides a powerful, neural-based solution for collective matrix factorization.
- The method effectively handles complex interactions and data heterogeneity in biomedical datasets.
- NCMF advances representation learning for seamless integration of multi-source biomedical information.
