ArticleBMC bioinformatics2010
Gene prioritization and clustering by multi-view text mining.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Comprehensive in silico analysis of genetic landscape and pathways involved in Stickler syndrome.PloS one · 2026Article
- Identification of pivotal genes and pathways in Chorea-acanthocytosis using comprehensive bioinformatic analysis.PloS one · 2024Article
- Identification of potential molecular mechanisms and candidate drugs for radiotherapy- and chemotherapy-induced mucositis.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2023Article
- A novel candidate disease gene prioritization method using deep graph convolutional networks and semi-supervised learning.BMC bioinformatics · 2022Article
- IoMT-Based Mitochondrial and Multifactorial Genetic Inheritance Disorder Prediction Using Machine Learning.Computational intelligence and neuroscience · 2022Article
- Angiogenesis goes computational - The future way forward to discover new angiogenic targets?Computational and structural biotechnology journal · 2022Review
- Identification of Key Genes and Molecular Pathways in Keratoconus: Integrating Text Mining and Bioinformatics Analysis.BioMed research international · 2022Article
- Identification of Key Genes and Pathways in Persistent Hyperplastic Primary Vitreous of the Eye Using Bioinformatic Analysis.Frontiers in medicine · 2021Article
- Computational screening of potential glioma-related genes and drugs based on analysis of GEO dataset and text mining.PloS one · 2021Article
- Identification of key genes and pathways in scleral extracellular matrix remodeling in glaucoma: Potential therapeutic agents discovered using bioinformatics analysis.International journal of medical sciences · 2021Article
- Mechanical Strain Regulates Myofibroblast Differentiation of Human Scleral Fibroblasts by YAP.Frontiers in physiology · 2021Article
- Text mining-based in silico drug discovery in oral mucositis caused by high-dose cancer therapy.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2018Article
- Potential Therapeutic Drugs for Parkinson's Disease Based on Data Mining and Bioinformatics Analysis.Parkinson's disease · 2018Article
- Graph-based unsupervised feature selection and multiview clustering for microarray data.Journal of biosciences · 2015Article
- Gene Prioritization by Compressive Data Fusion and Chaining.PLoS computational biology · 2015Article
- A random set scoring model for prioritization of disease candidate genes using protein complexes and data-mining of GeneRIF, OMIM and PubMed records.BMC bioinformatics · 2014Article
- A systems biology approach to the global analysis of transcription factors in colorectal cancer.BMC cancer · 2012Article
- Optimized data fusion for K-means Laplacian clustering.Bioinformatics (Oxford, England) · 2011Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundText mining has become a useful tool for biologists trying to understand the genetics of diseases. In particular, it can help identify the most interesting candidate genes for a disease for further experimental analysis. Many text mining approaches have been introduced, but the effect of disease-gene identification varies in different text mining models. Thus, the idea of incorporating more text mining models may be beneficial to obtain more refined and accurate knowledge. However, how to effectively combine these models still remains a challenging question in machine learning. In particular, it is a non-trivial issue to guarantee that the integrated model performs better than the best individual model.
resultsWe present a multi-view approach to retrieve biomedical knowledge using different controlled vocabularies. These controlled vocabularies are selected on the basis of nine well-known bio-ontologies and are applied to index the vast amounts of gene-based free-text information available in the MEDLINE repository. The text mining result specified by a vocabulary is considered as a view and the obtained multiple views are integrated by multi-source learning algorithms. We investigate the effect of integration in two fundamental computational disease gene identification tasks: gene prioritization and gene clustering. The performance of the proposed approach is systematically evaluated and compared on real benchmark data sets. In both tasks, the multi-view approach demonstrates significantly better performance than other comparing methods.
conclusionsIn practical research, the relevance of specific vocabulary pertaining to the task is usually unknown. In such case, multi-view text mining is a superior and promising strategy for text-based disease gene identification.
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