Graph-based semi-supervised learning with genomic data integration using condition-responsive genes applied to phenotype classification.
Objective: Data integration methods that combine data from different molecular levels such as genome, epigenome, transcriptome, etc., have received a great deal of interest in the past few years. It has been demonstrated that the synergistic effects of different biological data types can boost learn...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 25; no. 1; pp. 99 - 109 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Jan2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=127021684&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 127021684 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Jan2018 vid: 25 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 127021684 127021684 NLM28505320 127021684 10.1093/jamia/ocx032 NLM28505320 127021684 ppf: 99 ppct: 10 formats: tig: atl: Graph-based semi-supervised learning with genomic data integration using condition-responsive genes applied to phenotype classification. aug: au: Torshizi, Abolfazl Doostparast Petzold, Linda R Doostparast Torshizi, Abolfazl affil: Department of Computer Science, University of California, Santa Barbara, CA, USA sug: subj: Phenotype Ovarian Neoplasms Algorithms Gene Expression Genomics Metabolic Networks and Pathways Human DNA Methylation Bioinformatics Female Validation Studies Comparative Studies Evaluation Research Multicenter Studies Female ab: Objective: Data integration methods that combine data from different molecular levels such as genome, epigenome, transcriptome, etc., have received a great deal of interest in the past few years. It has been demonstrated that the synergistic effects of different biological data types can boost learning capabilities and lead to a better understanding of the underlying interactions among molecular levels.Methods: In this paper we present a graph-based semi-supervised classification algorithm that incorporates latent biological knowledge in the form of biological pathways with gene expression and DNA methylation data. The process of graph construction from biological pathways is based on detecting condition-responsive genes, where 3 sets of genes are finally extracted: all condition responsive genes, high-frequency condition-responsive genes, and P-value-filtered genes.Results: The proposed approach is applied to ovarian cancer data downloaded from the Human Genome Atlas. Extensive numerical experiments demonstrate superior performance of the proposed approach compared to other state-of-the-art algorithms, including the latest graph-based classification techniques.Conclusions: Simulation results demonstrate that integrating various data types enhances classification performance and leads to a better understanding of interrelations between diverse omics data types. The proposed approach outperforms many of the state-of-the-art data integration algorithms. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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