ArticleNucleic acids research2012
Co-clustering phenome-genome for phenotype classification and disease gene discovery.
Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Article
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- HetFCM: functional co-module discovery by heterogeneous network co-clustering.Nucleic acids research · 2024Article
- Computational Genomics in the Era of Precision Medicine: Applications to Variant Analysis and Gene Therapy.Journal of personalized medicine · 2022Review
- Network neighbors of viral targets and differentially expressed genes in COVID-19 are drug target candidates.Scientific reports · 2021Article
- Decoding Neuromuscular Disorders Using Phenotypic Clusters Obtained From Co-Occurrence Networks.Frontiers in molecular biosciences · 2021Article
- Identification of disease-associated loci using machine learning for genotype and network data integration.Bioinformatics (Oxford, England) · 2019Article
- Integrative construction of regulatory region networks in 127 human reference epigenomes by matrix factorization.Nucleic acids research · 2019Article
- TarGo: network based target gene selection system for human disease related mouse models.Laboratory animal research · 2019Article
- Cancer-mutation network and the number and specificity of driver mutations.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Graph-based semi-supervised learning with genomic data integration using condition-responsive genes applied to phenotype classification.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- ENPP1 and ESR1 genotypes associated with subclassifications of craniofacial asymmetry and severity of temporomandibular disorders.American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics · 2017Article
- Unified Alignment of Protein-Protein Interaction Networks.Scientific reports · 2017Article
- Context-sensitive network-based disease genetics prediction and its implications in drug discovery.Bioinformatics (Oxford, England) · 2017Article
- Network-based machine learning and graph theory algorithms for precision oncology.NPJ precision oncology · 2017Review
- Scalable non-negative matrix tri-factorization.BioData mining · 2017Article
- Identify bilayer modules via pseudo-3D clustering: applications to miRNA-gene bilayer networks.Nucleic acids research · 2016Article
- Biomechanisms of Comorbidity: Reviewing Integrative Analyses of Multi-omics Datasets and Electronic Health Records.Yearbook of medical informatics · 2016Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding the categorization of human diseases is critical for reliably identifying disease causal genes. Recently, genome-wide studies of abnormal chromosomal locations related to diseases have mapped >2000 phenotype-gene relations, which provide valuable information for classifying diseases and identifying candidate genes as drug targets. In this article, a regularized non-negative matrix tri-factorization (R-NMTF) algorithm is introduced to co-cluster phenotypes and genes, and simultaneously detect associations between the detected phenotype clusters and gene clusters. The R-NMTF algorithm factorizes the phenotype-gene association matrix under the prior knowledge from phenotype similarity network and protein-protein interaction network, supervised by the label information from known disease classes and biological pathways. In the experiments on disease phenotype-gene associations in OMIM and KEGG disease pathways, R-NMTF significantly improved the classification of disease phenotypes and disease pathway genes compared with support vector machines and Label Propagation in cross-validation on the annotated phenotypes and genes. The newly predicted phenotypes in each disease class are highly consistent with human phenotype ontology annotations. The roles of the new member genes in the disease pathways are examined and validated in the protein-protein interaction subnetworks. Extensive literature review also confirmed many new members of the disease classes and pathways as well as the predicted associations between disease phenotype classes and pathways.
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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.