Prediction and Validation of Disease Genes Using HeteSim Scores
Abstract
Deciphering the gene disease association is an important goal in biomedical research. In this paper, we use a novel relevance measure, called HeteSim, to prioritize candidate disease genes. Two methods based on heterogeneous networks constructed using protein-protein interaction, gene-phenotype associations, and phenotype-phenotype similarity, are presented. In HeteSim_MultiPath (HSMP), HeteSim scores of different paths are combined with a constant that dampens the contributions of longer paths. In HeteSim_SVM (HSSVM), HeteSim scores are combined with a machine learning method. The 3-fold experiments show that our non-machine learning method HSMP performs better than the existing non-machine learning methods, our machine learning method HSSVM obtains similar accuracy with the best existing machine learning method CATAPULT. From the analysis of the top 10 predicted genes for different diseases, we found that HSSVM avoid the disadvantage of the existing machine learning based methods, which always predict similar genes for different diseases. The data sets and Matlab code for the two methods are freely available for download at http://lab.malab.cn/data/HeteSim/index.jsp.
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Several researches proved that coding genes, noncoding RNAs, and proteins are related to diseases, including AD.
“Several researches proved that coding genes/noncoding RNAs/proteins were related to diseases, including AD [29,30,31,32,33,34,35,36].”
From An Efficient Classifier for Alzheimer’s Disease Genes Identification (2018) · cites this paper as [29]
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- Authors
- Zeng, X., Liao, Y., Liu, Y., & Zou, Q.
- Affiliations
- Xiamen University (CN)
- Dalian University of Technology (CN)
- Tianjin University (CN)
- Venue
- IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Published
- 2017-05-01
- Volume
- 14
- Issue
- 3
- Pages
- 687-695
- ISSN
- 1545-5963 (ISSN-L)
- OpenAlex
- W2274610632
- Cited by
- 208
- References
- 30
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 2 · 2023: 4 · 2022: 18 · 2021: 17 · 2020: 42 · 2019: 46 · 2018: 29 · 2017: 24 · 2016: 20
- FWCI
- 20.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Computational Biology and Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (61272152, 61370010)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- disease gene prediction, heterogeneous networks, gene-disease association, protein-protein interaction network, genotype-phenotype correlation
- MeSH terms
- Machine Learning, Genes, Humans, Reproducibility of Results, Models, Statistical, Genetic Predisposition to Disease, Genomics, Support Vector Machine
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