A novel method for predicting protein subcellular localization based on pseudo amino acid composition

Ma, Junwei; Gu, Hui

doi:10.3858/bmbrep.2010.43.10.670

Cited by 6 publications

(5 citation statements)

References 31 publications

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“…It is effectively used by various authors of the papers for the prediction of nuclear receptors [14], DNA binding sites [43] subcellular localization [44][45][46] enzyme functions [47][48][49] and GProtein-Coupled Receptors [20,21]. In this paper the pseudo amino acid composition is calculated by using the three properties: hydrophobicity (H1), hydrophilicity (H2) and side chain mass (M) of each 20 amino acid to represent the sequence order correlation between all of residues which are separated by 1 to 30 residues.…”

Section: Pseudo Amino Acid Composition (Paac)mentioning

confidence: 99%

An efficient approach for the prediction of ion channels and their subfamilies

Tiwari

Srivastava

2015

Computational Biology and Chemistry

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Section: Pseudo Amino Acid Composition (Paac)mentioning

confidence: 99%

An efficient approach for the prediction of ion channels and their subfamilies

Tiwari

Srivastava

2015

Computational Biology and Chemistry

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“…Some algorithms such as principal component analysis [46], minimal-redundancy-maximal-relevance (mRMR) [31], diffusion Maps [71] and the analysis of variance (ANOVA) [40] have been proposed for reducing the dimensionality. This study will introduce a new algorithm based on binomial distribution to optimize the feature sets [25].…”

Section: Feature Selectionmentioning

confidence: 99%

Predicting Subchloroplast Locations of Proteins Based on the General Form of Chou's Pseudo Amino Acid Composition: Approached From Optimal Tripeptide Composition

et al. 2013

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Chloroplasts are organelles found in plant cells that conduct photosynthesis. The subchloroplast locations of proteins are correlated with their functions. With the availability of a great number of protein data, it is highly desired to develop a computational method to predict the subchloroplast locations of chloroplast proteins. In this study, we proposed a novel method to predict subchloroplast locations of proteins using tripeptide compositions. It first used the binomial distribution to optimize the feature sets. Then the support vector machine was selected to perform the prediction of subchloroplast locations of proteins. The proposed method was tested on a reliable and rigorous dataset including 259 chloroplast proteins with sequence identity ≤ 25%. In the jack-knife cross-validation, 92.21% envelope proteins, 93.20% thylakoid membrane, 52.63% thylakoid lumen and 85.00% stroma can be correctly identified. The overall accuracy achieves 88.03% which is higher than that of other models. Based on this method, a predictor called ChloPred has been built and can be freely available §, ¶ Corresponding authors. 1350003-1 Int. J. Biomath. 2013.06. Downloaded from www.worldscientific.com by NANYANG TECHNOLOGICAL UNIVERSITY on 08/20/15. For personal use only. H. Lin et al.from http://cobi.uestc.edu.cn/people/hlin/tools/ChloPred/. The predictor will provide important information for theoretical and experimental research of chloroplast proteins.

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“…A lot of methods have been proposed to identify and predict the subcellular location(s) of a protein in different organisms [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. One of the most practical approaches is the KNN algorithm, which is simple and powerful in identifying various protein attributes, such as optimized evidencetheoretic KNN (OET-KNN) [4,8], weighted KNN [17,18], fuzzy KNN (FKNN) [5,19], etc.…”

Section: Introductionmentioning

confidence: 99%

Predicting Viral Protein Subcellular Localization with Chou's Pseudo Amino Acid Composition and Imbalance-Weighted Multi-Label K-Nearest Neighbor Algorithm

Cao

Liu²,

2012

PPL

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Machine learning is a kind of reliable technology for automated subcellular localization of viral proteins within a host cell or virus-infected cell. One challenge is that the viral protein samples are not only with multiple location sites, but also class-imbalanced. The imbalanced dataset often decreases the prediction performance. In order to accomplish this challenge, this paper proposes a novel approach named imbalance-weighted multi-label K-nearest neighbor to predict viral protein subcellular location with multiple sites. The experimental results by jackknife test indicate that the presented algorithm achieves a better performance than the existing methods and has great potentials in protein science.

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A novel method for predicting protein subcellular localization based on pseudo amino acid composition

Abstract: In this paper, a novel approach, ELM-PCA, is introduced for the first time to predict protein subcellular localization.

Cited by 6 publications

References 31 publications

An efficient approach for the prediction of ion channels and their subfamilies

An efficient approach for the prediction of ion channels and their subfamilies

Predicting Subchloroplast Locations of Proteins Based on the General Form of Chou's Pseudo Amino Acid Composition: Approached From Optimal Tripeptide Composition

Predicting Viral Protein Subcellular Localization with Chou's Pseudo Amino Acid Composition and Imbalance-Weighted Multi-Label K-Nearest Neighbor Algorithm

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