ArticleBioMed research international2014
Approaches for recognizing disease genes based on network.
Article in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- LanDis: the disease landscape explorer.European journal of human genetics : EJHG · 2024Article
- Predicting Microbe-Disease Association by Learning Graph Representations and Rule-Based Inference on the Heterogeneous Network.Frontiers in microbiology · 2020Article
- Heterogeneous network embedding enabling accurate disease association predictions.BMC medical genomics · 2019Article
- Predicting links between tumor samples and genes using 2-Layered graph based diffusion approach.BMC bioinformatics · 2019Article
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- Integrating Multiple Interaction Networks for Gene Function Inference.Molecules (Basel, Switzerland) · 2018Article
- Application of Molecular Methods in the Identification of Ingredients in Chinese Herbal Medicines.Molecules (Basel, Switzerland) · 2018Review
- Noncoding Variants Functional Prioritization Methods Based on Predicted Regulatory Factor Binding Sites.Current genomics · 2017Review
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- A novel approach for predicting microbe-disease associations by bi-random walk on the heterogeneous network.PloS one · 2017Article
- Interspecies gene function prediction using semantic similarity.BMC systems biology · 2016Article
- Computational identification of putative lincRNAs in mouse embryonic stem cell.Scientific reports · 2016Article
- Differential network analysis from cross-platform gene expression data.Scientific reports · 2016Article
- Complex Network Clustering by a Multi-objective Evolutionary Algorithm Based on Decomposition and Membrane Structure.Scientific reports · 2016Article
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- Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology.International journal of genomics · 2016Article
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- Article
- DNA binding protein identification by combining pseudo amino acid composition and profile-based protein representation.Scientific reports · 2015Article
- Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods.BioMed research international · 2015Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diseases are closely related to genes, thus indicating that genetic abnormalities may lead to certain diseases. The recognition of disease genes has long been a goal in biology, which may contribute to the improvement of health care and understanding gene functions, pathways, and interactions. However, few large-scale gene-gene association datasets, disease-disease association datasets, and gene-disease association datasets are available. A number of machine learning methods have been used to recognize disease genes based on networks. This paper states the relationship between disease and gene, summarizes the approaches used to recognize disease genes based on network, analyzes the core problems and challenges of the methods, and outlooks future research direction.
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