ArticleBMC genomics2017
Predicting disease-related genes using integrated biomedical networks.
Article in BMC genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 57 citations in OpenAlex.
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- Potential Schizophrenia Disease-Related Genes Prediction Using Metagraph Representations Based on a Protein-Protein Interaction Keyword Network: Framework Development and Validation.JMIR formative research · 2023Article
- Predicting disease genes based on multi-head attention fusion.BMC bioinformatics · 2023Article
- Review
- HetIG-PreDiG: A Heterogeneous Integrated Graph Model for Predicting Human Disease Genes based on gene expression.PloS one · 2023Article
- The module triad: a novel network biology approach to utilize patients' multi-omics data for target discovery in ulcerative colitis.Scientific reports · 2022Article
- GediNET for discovering gene associations across diseases using knowledge based machine learning approach.Scientific reports · 2022Article
- Benchmarking network-based gene prioritization methods for cerebral small vessel disease.Briefings in bioinformatics · 2021Article
- Integration of anatomy ontology data with protein-protein interaction networks improves the candidate gene prediction accuracy for anatomical entities.BMC bioinformatics · 2020Article
- A Knowledge-Based Machine Learning Approach to Gene Prioritisation in Amyotrophic Lateral Sclerosis.Genes · 2020Article
- Predicting the Disease Genes of Multiple Sclerosis Based on Network Representation Learning.Frontiers in genetics · 2020Article
- LncRNA2Target v2.0: a comprehensive database for target genes of lncRNAs in human and mouse.Nucleic acids research · 2019Article
- Predicting Parkinson's Disease Genes Based on Node2vec and Autoencoder.Frontiers in genetics · 2019Article
- Measuring phenotype-phenotype similarity through the interactome.BMC bioinformatics · 2018Article
- Improving the measurement of semantic similarity by combining gene ontology and co-functional network: a random walk based approach.BMC systems biology · 2018Article
- Identifying disease genes using machine learning and gene functional similarities, assessed through Gene Ontology.PloS one · 2018Article
- Identifying term relations cross different gene ontology categories.BMC bioinformatics · 2017Article
- Investigations on factors influencing HPO-based semantic similarity calculation.Journal of biomedical semantics · 2017Article
- Dynamically analyzing cell interactions in biological environments using multiagent social learning framework.Journal of biomedical semantics · 2017Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundIdentifying the genes associated to human diseases is crucial for disease diagnosis and drug design. Computational approaches, esp. the network-based approaches, have been recently developed to identify disease-related genes effectively from the existing biomedical networks. Meanwhile, the advance in biotechnology enables researchers to produce multi-omics data, enriching our understanding on human diseases, and revealing the complex relationships between genes and diseases. However, none of the existing computational approaches is able to integrate the huge amount of omics data into a weighted integrated network and utilize it to enhance disease related gene discovery.
resultsWe propose a new network-based disease gene prediction method called SLN-SRW (Simplified Laplacian Normalization-Supervised Random Walk) to generate and model the edge weights of a new biomedical network that integrates biomedical data from heterogeneous sources, thus far enhancing the disease related gene discovery.
conclusionsThe experiment results show that SLN-SRW significantly improves the performance of disease gene prediction on both the real and the synthetic data sets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.