Prediction of protein subcellular localization by incorporating multiobjective PSO-based feature subset selection into the general form of Chou's PseAAC.
In this article, the possible subcellular location of a protein is predicted using multiobjective particle swarm optimization-based feature selection technique. In general form of pseudo-amino acid composition, the protein sequences are used for constructing protein features. Here, the different ami...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 53; no. 4; pp. 331 - 345 |
|---|---|
| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Apr2015
|
| 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=109710306&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109710306 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Apr2015 vid: 53 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 109710306 NLM25564182 2012940760 10.1007/s11517-014-1238-7 NLM25564182 109710306 ppf: 331 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Prediction of protein subcellular localization by incorporating multiobjective PSO-based feature subset selection into the general form of Chou's PseAAC. aug: au: Mandal, Monalisa Mukhopadhyay, Anirban Maulik, Ujjwal sug: ab: In this article, the possible subcellular location of a protein is predicted using multiobjective particle swarm optimization-based feature selection technique. In general form of pseudo-amino acid composition, the protein sequences are used for constructing protein features. Here, the different amino acids compositions are used to construct the feature sets. Therefore, the data are presented as sample of protein versus amino acid compositions as features. The proposed algorithm tries to maximize the feature relevance and minimize the feature redundancy simultaneously. After proposed algorithm is executed on the multiclass dataset, some features are selected. On this resultant feature subset, tenfold cross-validation is applied and corresponding accuracy, F score, entropy, representation entropy and average correlation are calculated. The performance of the proposed method is compared with that of its single objective versions, sequential forward search, sequential backward search, minimum redundancy maximum relevance with two schemes, CFS, CBFS, [Formula: see text], Fisher discriminant and a Cluster-based technique. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|