Predicting Antibacterial Peptides by the Concept of Chou’s Pseudo-amino Acid Composition and Machine Learning Methods

Khosravian, Maede; Faramarzi, Fateme Kazemi; Beigi, Majid Mohammad; Behbahani, Mandana; Mohabatkar, Hassan

doi:10.2174/0929866511320020009

Cited by 22 publications

(25 citation statements)

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“…Accordingly the comparison was limited in identifying AMPs or non-AMPs only. Also, the methods proposed in [12] and [13] did not provide any web-server, while the method in [9,10] were limited for antibacterial peptides only. To make it feasible and meaningful, the comparison was performed with the CAMP method [11], which contained three different algorithms or operation engines: the Support Vector Machine, Random Forests, and Discriminant Analysis.…”

Section: Resultsmentioning

confidence: 99%

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iAMP-2L: A two-level multi-label classifier for identifying antimicrobial peptides and their functional types

Xiao

Wang

Wei

et al. 2013

Analytical Biochemistry

472

331

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Section: Resultsmentioning

confidence: 99%

“…Subsequently, Wang et al [12] proposed a new method for predicting AMPs by integrating the sequence alignment method with the feature selection method. Recently, Mohabatkar and coworkers proposed a new method for predicting AMPs peptides based on the concept of Chou's pseudo-amino acid composition and machine learning methods [13].…”

Section: Introductionmentioning

confidence: 99%

iAMP-2L: A two-level multi-label classifier for identifying antimicrobial peptides and their functional types

Xiao

Wang

Wei

et al. 2013

Analytical Biochemistry

472

331

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“…Applying six characters and their combination produced 126 features of per peptide that were used to classify dataset [66].…”

Section: Producing Chou's Pseaacmentioning

confidence: 99%

Using Chou’s Pseudo Amino Acid Composition and Machine Learning Method to Predict the Antiviral Peptides

Zare¹,

Mohabatkar²,

Faramarzi³

et al. 2015

TOBIOIJ

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Traditional antiviral therapies are expensive, limitedly available, and cause several side effects. Currently, designing antiviral peptides is very important, because these peptides interfere with the key stage of virus life cycle. Most of the antiviral peptides are derived from viral proteins for example peptide derived from HIV-1 capsid protein. Because of the importance of these peptides, in this study the concept of pseudo-amino acid composition (PseAAC) and machine learning methods are used to classify or identify antiviral peptides.

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“…To avoid completely losing the sequence-order information, the pseudo amino acid composition (PseAAC) was proposed [21] to replace the simple amino acid composition (AAC) for representing the sample of a protein. Since the concept of PseAAC was proposed in 2001, it has penetrated into almost all the fields of protein attribute predictions (see, e.g, [22][23][24][25][26][27][28][29][30][31][32][33][34] as well as a long list of references cited in a review paper [35] ]). Recently, the concept of PseAAC was further extended to represent the feature vectors of DNA and nucleotides, [36][37] as well as other biological samples (see, e.g., the literature [38][39][40] ).…”

Section: Introductionmentioning

confidence: 99%

Protein Remote Homology Detection by Combining Chou’s Pseudo Amino Acid Composition and Profile‐Based Protein Representation

Liu

Wang

Zou

et al. 2013

Molecular Informatics

108

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Protein remote homology detection is a key problem in bioinformatics. Currently the discriminative methods, such as Support Vector Machine (SVM) can achieve the best performance. The most efficient approach to improve the performance of SVM-based methods is to find a general protein representation method that is able to convert proteins with different lengths into fixed length vectors and captures the different properties of the proteins for the discrimination. The bottleneck of designing the protein representation method is that native proteins have different lengths. Motivated by the success of the pseudo amino acid composition (PseAAC) proposed by Chou, we applied this approach for protein remote homology detection. Some new indices derived from the amino acid index (AAIndex) database are incorporated into the PseAAC to improve the generalization ability of this method. Finally, the performance is further improved by combining the modified PseAAC with profile-based protein representation containing the evolutionary information extracted from the frequency profiles. Our experiments on a well-known benchmark show this method achieves superior or comparable performance with current state-of-the-art methods.

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Predicting Antibacterial Peptides by the Concept of Chou’s Pseudo-amino Acid Composition and Machine Learning Methods

Cited by 22 publications

References 0 publications

iAMP-2L: A two-level multi-label classifier for identifying antimicrobial peptides and their functional types

iAMP-2L: A two-level multi-label classifier for identifying antimicrobial peptides and their functional types

Using Chou’s Pseudo Amino Acid Composition and Machine Learning Method to Predict the Antiviral Peptides

Protein Remote Homology Detection by Combining Chou’s Pseudo Amino Acid Composition and Profile‐Based Protein Representation

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