Literature DB >> 23546013

A multilabel model based on Chou's pseudo-amino acid composition for identifying membrane proteins with both single and multiple functional types.

Chao Huang1, Jing-Qi Yuan.   

Abstract

Predicting membrane protein type is a meaningful task because this kind of information is very useful to explain the function of membrane proteins. Due to the explosion of new protein sequences discovered, it is highly desired to develop efficient computation tools for quickly and accurately predicting the membrane type for a given protein sequence. Even though several membrane predictors have been developed, they can only deal with the membrane proteins which belong to the single membrane type. The fact is that there are membrane proteins belonging to two or more than two types. To solve this problem, a system for predicting membrane protein sequences with single or multiple types is proposed. Pseudo-amino acid composition, which has proven to be a very efficient tool in representing protein sequences, and a multilabel KNN algorithm are used to compose this prediction engine. The results of this initial study are encouraging.

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Year:  2013        PMID: 23546013     DOI: 10.1007/s00232-013-9536-9

Source DB:  PubMed          Journal:  J Membr Biol        ISSN: 0022-2631            Impact factor:   1.843


  53 in total

1.  Multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization.

Authors:  Suyu Mei
Journal:  J Theor Biol       Date:  2011-10-21       Impact factor: 2.691

2.  Predicting protein solubility by the general form of Chou's pseudo amino acid composition: approached from chaos game representation and fractal dimension.

Authors:  Xiao-Hui Niu; Xue-Hai Hu; Feng Shi; Jing-Bo Xia
Journal:  Protein Pept Lett       Date:  2012-09       Impact factor: 1.890

3.  Wavelet images and Chou's pseudo amino acid composition for protein classification.

Authors:  Loris Nanni; Sheryl Brahnam; Alessandra Lumini
Journal:  Amino Acids       Date:  2011-10-13       Impact factor: 3.520

4.  Gneg-mPLoc: a top-down strategy to enhance the quality of predicting subcellular localization of Gram-negative bacterial proteins.

Authors:  Hong-Bin Shen; Kuo-Chen Chou
Journal:  J Theor Biol       Date:  2010-01-20       Impact factor: 2.691

5.  Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses.

Authors:  Maryam Esmaeili; Hassan Mohabatkar; Sasan Mohsenzadeh
Journal:  J Theor Biol       Date:  2009-12-02       Impact factor: 2.691

6.  Predicting antibacterial peptides by the concept of Chou's pseudo-amino acid composition and machine learning methods.

Authors:  Maede Khosravian; Fateme Kazemi Faramarzi; Majid Mohammad Beigi; Mandana Behbahani; Hassan Mohabatkar
Journal:  Protein Pept Lett       Date:  2013-02       Impact factor: 1.890

7.  Plant-mPLoc: a top-down strategy to augment the power for predicting plant protein subcellular localization.

Authors:  Kuo-Chen Chou; Hong-Bin Shen
Journal:  PLoS One       Date:  2010-06-28       Impact factor: 3.240

8.  NR-2L: a two-level predictor for identifying nuclear receptor subfamilies based on sequence-derived features.

Authors:  Pu Wang; Xuan Xiao; Kuo-Chen Chou
Journal:  PLoS One       Date:  2011-08-15       Impact factor: 3.240

9.  Using cellular automata images and pseudo amino acid composition to predict protein subcellular location.

Authors:  X Xiao; S Shao; Y Ding; Z Huang; K-C Chou
Journal:  Amino Acids       Date:  2005-07-28       Impact factor: 3.520

10.  Some remarks on protein attribute prediction and pseudo amino acid composition.

Authors:  Kuo-Chen Chou
Journal:  J Theor Biol       Date:  2010-12-17       Impact factor: 2.691

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  19 in total

1.  Predicting the Functional Types of Singleplex and Multiplex Eukaryotic Membrane Proteins via Different Models of Chou's Pseudo Amino Acid Compositions.

Authors:  Hong-Liang Zou; Xuan Xiao
Journal:  J Membr Biol       Date:  2015-10-12       Impact factor: 1.843

2.  TargetFreeze: Identifying Antifreeze Proteins via a Combination of Weights using Sequence Evolutionary Information and Pseudo Amino Acid Composition.

Authors:  Xue He; Ke Han; Jun Hu; Hui Yan; Jing-Yu Yang; Hong-Bin Shen; Dong-Jun Yu
Journal:  J Membr Biol       Date:  2015-06-10       Impact factor: 1.843

3.  A new multi-label classifier in identifying the functional types of human membrane proteins.

Authors:  Hong-Liang Zou; Xuan Xiao
Journal:  J Membr Biol       Date:  2014-11-30       Impact factor: 1.843

4.  iMem-Seq: A Multi-label Learning Classifier for Predicting Membrane Proteins Types.

Authors:  Xuan Xiao; Hong-Liang Zou; Wei-Zhong Lin
Journal:  J Membr Biol       Date:  2015-03-22       Impact factor: 1.843

Review 5.  A Treatise to Computational Approaches Towards Prediction of Membrane Protein and Its Subtypes.

Authors:  Ahmad Hassan Butt; Nouman Rasool; Yaser Daanial Khan
Journal:  J Membr Biol       Date:  2016-11-19       Impact factor: 1.843

6.  Predicting membrane proteins and their types by extracting various sequence features into Chou's general PseAAC.

Authors:  Ahmad Hassan Butt; Nouman Rasool; Yaser Daanial Khan
Journal:  Mol Biol Rep       Date:  2018-09-20       Impact factor: 2.316

7.  A multi-label classifier for prediction membrane protein functional types in animal.

Authors:  Hong-Liang Zou
Journal:  J Membr Biol       Date:  2014-08-09       Impact factor: 1.843

8.  Classifying Multifunctional Enzymes by Incorporating Three Different Models into Chou's General Pseudo Amino Acid Composition.

Authors:  Hong-Liang Zou; Xuan Xiao
Journal:  J Membr Biol       Date:  2016-04-25       Impact factor: 1.843

Review 9.  Some illuminating remarks on molecular genetics and genomics as well as drug development.

Authors:  Kuo-Chen Chou
Journal:  Mol Genet Genomics       Date:  2020-01-01       Impact factor: 3.291

10.  iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking.

Authors:  Xuan Xiao; Jian-Liang Min; Pu Wang; Kuo-Chen Chou
Journal:  PLoS One       Date:  2013-08-27       Impact factor: 3.240

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