Incorporating Pathway Information into Feature Selection towards Better Performed Gene Signatures.
To analyze gene expression data with sophisticated grouping structures and to extract hidden patterns from such data, feature selection is of critical importance. It is well known that genes do not function in isolation but rather work together within various metabolic, regulatory, and signaling pat...
| Publicado en: | BioMed Research International pp. 1 - 13 |
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| Autores principales: | , , |
| Formato: | equations & formulas pictorial review tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
4/3/2019
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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=135702410&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135702410 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 4/3/2019 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 135702410 135702410 135702410 10.1155/2019/2497509 135702410 ppf: 1 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Incorporating Pathway Information into Feature Selection towards Better Performed Gene Signatures. aug: au: Tian, Suyan Wang, Chi Wang, Bing affil: Division of Clinical Research, The First Hospital of Jilin University, 71 Xinmin Street, Changchun, Jilin 130021, China sug: subj: Signal Transduction Gene Expression Evaluation Genes Metabolic Networks and Pathways Bioinformatics Algorithms ab: To analyze gene expression data with sophisticated grouping structures and to extract hidden patterns from such data, feature selection is of critical importance. It is well known that genes do not function in isolation but rather work together within various metabolic, regulatory, and signaling pathways. If the biological knowledge contained within these pathways is taken into account, the resulting method is a pathway-based algorithm. Studies have demonstrated that a pathway-based method usually outperforms its gene-based counterpart in which no biological knowledge is considered. In this article, a pathway-based feature selection is firstly divided into three major categories, namely, pathway-level selection, bilevel selection, and pathway-guided gene selection. With bilevel selection methods being regarded as a special case of pathway-guided gene selection process, we discuss pathway-guided gene selection methods in detail and the importance of penalization in such methods. Last, we point out the potential utilizations of pathway-guided gene selection in one active research avenue, namely, to analyze longitudinal gene expression data. We believe this article provides valuable insights for computational biologists and biostatisticians so that they can make biology more computable. pubtype: Academic Journal doctype: equations & formulas pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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