ArticleBMC medical genomics2017
Probability-based collaborative filtering model for predicting gene-disease associations.
Article in BMC medical genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Computational prediction of new therapeutic effects of probiotics.Scientific reports · 2024Article
- Predicting disease genes based on multi-head attention fusion.BMC bioinformatics · 2023Article
- HetIG-PreDiG: A Heterogeneous Integrated Graph Model for Predicting Human Disease Genes based on gene expression.PloS one · 2023Article
- Task-driven knowledge graph filtering improves prioritizing drugs for repurposing.BMC bioinformatics · 2022Article
- M2PP: a novel computational model for predicting drug-targeted pathogenic proteins.BMC bioinformatics · 2022Article
- A GHKNN model based on the physicochemical property extraction method to identify SNARE proteins.Frontiers in genetics · 2022Article
- HSM6AP: a high-precision predictor for the HomoRNA biology · 2021Article
- Identifying potential association on gene-disease network via dual hypergraph regularized least squares.BMC genomics · 2021Article
- Graph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus.Frontiers in genetics · 2021Article
- Comprehensive host-pathogen protein-protein interaction network analysis.BMC bioinformatics · 2020Article
- Article
- A novel collaborative filtering model for LncRNA-disease association prediction based on the Naïve Bayesian classifier.BMC bioinformatics · 2019Article
- Molecular Network-Based Drug Prediction in Thyroid Cancer.International journal of molecular sciences · 2019Article
- An Effective Method to Measure Disease Similarity Using Gene and Phenotype Associations.Frontiers in genetics · 2019Article
- Discovering Cancer Subtypes via an Accurate Fusion Strategy on Multiple Profile Data.Frontiers in genetics · 2019Article
- k-Skip-n-Gram-RF: A Random Forest Based Method for Alzheimer's Disease Protein Identification.Frontiers in genetics · 2019Article
- Identification of Triple-Negative Breast Cancer Genes and a Novel High-Risk Breast Cancer Prediction Model Development Based on PPI Data and Support Vector Machines.Frontiers in genetics · 2019Article
- Identification of Phage Viral Proteins With Hybrid Sequence Features.Frontiers in microbiology · 2019Article
- Identifying Disease-Gene Associations With Graph-Regularized Manifold Learning.Frontiers in genetics · 2019Article
- A Bidirectional Label Propagation Based Computational Model for Potential Microbe-Disease Association Prediction.Frontiers in microbiology · 2019Article
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4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene-disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses.
methodsWe propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our model. Firstly, on the basis of a typical latent factorization model, we propose model I with an average heterogeneous regularization. Secondly, we develop modified model II with personal heterogeneous regularization to enhance the accuracy of aforementioned models. In this model, vector space similarity or Pearson correlation coefficient metrics and data on related species are also used.
resultsWe compared the results of PCFM with the results of four state-of-arts approaches. The results show that PCFM performs better than other advanced approaches.
conclusionsPCFM model can be leveraged for predictions of disease genes, especially for new human genes or diseases with no known relationships.
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