Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition

Lin, Hao; Ding, Hui

doi:10.1016/j.jtbi.2010.10.019

Cited by 145 publications

(87 citation statements)

References 80 publications

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“…However, as elucidated by Chou and Shen (2008) and demonstrated in Chou and Shen (2007), among the three cross-validation methods, the jackknife test is deemed the most objective one (Feng 2002) and can always yield a unique result for a given benchmark data set; hence, it has been increasingly used by investigators to examine the accuracy of various predictors (Zhou 1998;Zhou and Assa-Munt 2001;Zhou and Doctor 2003;Zhou et al 2007;Jiang et al 2008a, b;Li and Li 2008b;Lin 2008;Lin et al 2008;Zhang and Fang 2008;Bi et al 2011;Ding et al 2011;Hayat and Khan 2011;Hu et al 2011;Joshi and Sekharan 2010;Kandaswamy et al 2010;Kandaswamy et al 2011;Lin and Ding 2011;Liu et al 2010;Zakeri et al 2011). During the jackknife test process, each protein is singled out in turn as a test sample; the remaining proteins are used as a training set to calculate the test sample's membership and predict the class.…”

Section: Evaluation Methodsmentioning

confidence: 99%

Predicting protein submitochondria locations by combining different descriptors into the general form of Chou’s pseudo amino acid composition

Fan

2011

Amino Acids

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Knowledge of the submitochondria location of protein is integral to understanding its function and a necessity in the proteomics era. In this work, a new submitochondria data set is constructed, and an approach for predicting protein submitochondria locations is proposed by combining the amino acid composition, dipeptide composition, reduced physicochemical properties, gene ontology, evolutionary information, and pseudo-average chemical shift. The overall prediction accuracy is 93.57% for the submitochondria location and 97.79% for the three membrane protein types in the mitochondria inner membrane using the algorithm of the increment of diversity combined with the support vector machine. The performance of the pseudo-average chemical shift is excellent. For contrast, the method is also used to predict submitochondria locations in the data set constructed by Du and Li; an accuracy of 94.95% is obtained by our method, which is better than that of other existing methods.

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Section: Evaluation Methodsmentioning

confidence: 99%

Predicting protein submitochondria locations by combining different descriptors into the general form of Chou’s pseudo amino acid composition

Fan

2011

Amino Acids

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“…[42,50,[69][70][71][72][73]). The basic idea of SVM is to transform the data into a high-dimensional feature space and then determine the optimal separating hyperplane using a kernel function.…”

Section: Support Vector Machinementioning

confidence: 99%

“…One of the most simple PseAAC modes is the so-called n-peptide composition: when n = 1, it is reduced to AAC; when n = 2, it is reduced to dipeptide composition [28,30,[50][51][52]; when n = 3, it is reduced to tripeptide composition [53]; and so forth. Although the n-peptide composition can incorporate some sort of sequence order information when n P 2, the dimension of PseAAC formed in this way will increase rapidly.…”

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confidence: 99%

iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition

Feng

Chen

Lin

et al. 2013

Analytical Biochemistry

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131

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“…Classification protocol SVM is a very powerful and popular method for supervised pattern recognition and has been widely used in the realm of bioinformatics [3,6,29,37,38,42,[44][45][46]. To handle a multiclass problem, the one-versus-one (OVO) and oneversus-rest (OVR) approaches are generally applied to extend traditional SVM.…”

Section: Frequency Of Reduced Amino Acid Alphabetmentioning

confidence: 99%

Prediction of ketoacyl synthase family using reduced amino acid alphabets

Chen

Feng

Lin

2012

Journal of Industrial Microbiology and Biotechnology

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Ketoacyl synthases are enzymes involved in fatty acid synthesis and can be classified into five families based on primary sequence similarity. Different families have different catalytic mechanisms. Developing cost-effective computational models to identify the family of ketoacyl synthases will be helpful for enzyme engineering and in knowing individual enzymes' catalytic mechanisms. In this work, a support vector machine-based method was developed to predict ketoacyl synthase family using the n-peptide composition of reduced amino acid alphabets. In jackknife cross-validation, the model based on the 2-peptide composition of a reduced amino acid alphabet of size 13 yielded the best overall accuracy of 96.44% with average accuracy of 93.36%, which is superior to other state-of-the-art methods. This result suggests that the information provided by n-peptide compositions of reduced amino acid alphabets provides efficient means for enzyme family classification and that the proposed model can be efficiently used for ketoacyl synthase family annotation.

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Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition

Cited by 145 publications

References 80 publications

Predicting protein submitochondria locations by combining different descriptors into the general form of Chou’s pseudo amino acid composition

Predicting protein submitochondria locations by combining different descriptors into the general form of Chou’s pseudo amino acid composition

iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition

Prediction of ketoacyl synthase family using reduced amino acid alphabets

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