Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou's General Pseudo Amino Acid Composition.
Mitochondrion is the key organelle of eukaryotic cell, which provides energy for cellular activities. Submitochondrial locations of proteins play crucial role in understanding different biological processes such as energy metabolism, program cell death, and ionic homeostasis. Prediction of submitoch...
| Published in: | Journal of Membrane Biology Vol. 249; no. 3; pp. 293 - 305 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Jun2016
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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=115530847&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115530847 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00222631 O6G jtl: Journal of Membrane Biology issn: 00222631 maglogo: N pubinfo: dt: Jun2016 vid: 249 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115530847 115530847 NLM26746980 10.1007/s00232-015-9868-8 NLM26746980 115530847 ppf: 293 ppct: 12 formats: tig: atl: Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou's General Pseudo Amino Acid Composition. aug: au: Ahmad, Khurshid Waris, Muhammad Hayat, Maqsood affil: Department of Computer Science, Abdul Wali Khan University Mardan, Mardan Pakistan sug: subj: Proteins Metabolism Mitochondria Metabolism Proteins Oligopeptides Metabolism Amino Acids Reproducibility of Results Resource Databases Neural Networks (Computer) Sensitivity and Specificity Biological Transport Bioinformatics Methods Algorithms Factor Analysis Clinical Assessment Tools Questionnaires ab: Mitochondrion is the key organelle of eukaryotic cell, which provides energy for cellular activities. Submitochondrial locations of proteins play crucial role in understanding different biological processes such as energy metabolism, program cell death, and ionic homeostasis. Prediction of submitochondrial locations through conventional methods are expensive and time consuming because of the large number of protein sequences generated in the last few decades. Therefore, it is intensively desired to establish an automated model for identification of submitochondrial locations of proteins. In this regard, the current study is initiated to develop a fast, reliable, and accurate computational model. Various feature extraction methods such as dipeptide composition (DPC), Split Amino Acid Composition, and Composition and Translation were utilized. In order to overcome the issue of biasness, oversampling technique SMOTE was applied to balance the datasets. Several classification learners including K-Nearest Neighbor, Probabilistic Neural Network, and support vector machine (SVM) are used. Jackknife test is applied to assess the performance of classification algorithms using two benchmark datasets. Among various classification algorithms, SVM achieved the highest success rates in conjunction with the condensed feature space of DPC, which are 95.20 % accuracy on dataset SML3-317 and 95.11 % on dataset SML3-983. The empirical results revealed that our proposed model obtained the highest results so far in the literatures. It is anticipated that our proposed model might be useful for future studies. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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