FSim: a novel functional similarity search algorithm and tool for discovering functionally related gene products.
Background: During the analysis of genomics data, it is often required to quantify the functional similarity of genes and their products based on the annotation information from gene ontology (GO) with hierarchical structure. A flexible and user-friendly way to estimate the functional similarity of...
| Published in: | BioMed Research International Vol. 2014; pp. 509149 - 509150 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Published: |
Wiley-Blackwell
2014
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109676857&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109676857 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2014 vid: 2014 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109676857 100579451 NLM25184141 2012705031 10.1155/2014/509149 NLM25184141 PMC4145548 109676857 ppf: 509149 ppct: 1 formats: fmt: @attributes: type: P tig: atl: FSim: a novel functional similarity search algorithm and tool for discovering functionally related gene products. aug: au: Hu, Qiang Wang, ZhiGang Zhang, ZhengGuo sug: ab: Background: During the analysis of genomics data, it is often required to quantify the functional similarity of genes and their products based on the annotation information from gene ontology (GO) with hierarchical structure. A flexible and user-friendly way to estimate the functional similarity of genes utilizing GO annotation is therefore highly desired.Results: We proposed a novel algorithm using a level coefficient-weighted model to measure the functional similarity of gene products based on multiple ontologies of hierarchical GO annotations. The performance of our algorithm was evaluated and found to be superior to the other tested methods. We implemented the proposed algorithm in a software package, FSim, based on R statistical and computing environment. It can be used to discover functionally related genes for a given gene, group of genes, or set of function terms.Conclusions: FSim is a flexible tool to analyze functional gene groups based on the GO annotation databases. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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