ArticleThe Journal of membrane biology2016
Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou's General Pseudo Amino Acid Composition.
Article in The Journal of membrane biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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33 citing papers in PubMed, 98 citations in OpenAlex.
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- Some illuminating remarks on molecular genetics and genomics as well as drug development.Molecular genetics and genomics : MGG · 2020Review
- Recognition of Mitochondrial Proteins in Plasmodium Based on the Tripeptide Composition.Frontiers in cell and developmental biology · 2020Article
- Identifying Heat Shock Protein Families from Imbalanced Data by Using Combined Features.Computational and mathematical methods in medicine · 2020Article
- iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites.Briefings in bioinformatics · 2019Review
- Prediction of Apoptosis Protein Subcellular Localization with Multilayer Sparse Coding and Oversampling Approach.BioMed research international · 2019Article
- Prediction of Protein Sub-Mitochondria Locations Using Protein Interaction Networks.Iranian journal of biotechnology · 2018Article
- PrESOgenesis: A two-layer multi-label predictor for identifying fertility-related proteins using support vector machine and pseudo amino acid composition approach.Scientific reports · 2018Article
- iRNA-3typeA: Identifying Three Types of Modification at RNA's Adenosine Sites.Molecular therapy. Nucleic acids · 2018Article
- Estimation of sodium adsorption ratio indicator using data mining methods: a case study in Urmia Lake basin, Iran.Environmental science and pollution research international · 2018Article
- UltraPse: A Universal and Extensible Software Platform for Representing Biological Sequences.International journal of molecular sciences · 2017Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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