Literature DB >> 23395824

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

Xuan Xiao1, Pu Wang, Wei-Zhong Lin, Jian-Hua Jia, Kuo-Chen Chou.   

Abstract

Antimicrobial peptides (AMPs), also called host defense peptides, are an evolutionarily conserved component of the innate immune response and are found among all classes of life. According to their special functions, AMPs are generally classified into ten categories: Antibacterial Peptides, Anticancer/tumor Peptides, Antifungal Peptides, Anti-HIV Peptides, Antiviral Peptides, Antiparasital Peptides, Anti-protist Peptides, AMPs with Chemotactic Activity, Insecticidal Peptides, and Spermicidal Peptides. Given a query peptide, how can we identify whether it is an AMP or non-AMP? If it is, can we identify which functional type or types it belong to? Particularly, how can we deal with the multi-type problem since an AMP may belong to two or more functional types? To address these problems, which are obviously very important to both basic research and drug development, a multi-label classifier was developed based on the pseudo amino acid composition (PseAAC) and fuzzy K-nearest neighbor (FKNN) algorithm, where the components of PseAAC were featured by incorporating five physicochemical properties. The novel classifier is called iAMP-2L, where "2L" means that it is a 2-level predictor. The 1st-level is to answer the 1st question above, while the 2nd-level is to answer the 2nd and 3rd questions that are beyond the reach of any existing methods in this area. For the conveniences of users, a user-friendly web-server for iAMP-2L was established at http://www.jci-bioinfo.cn/iAMP-2L.
Copyright © 2013 Elsevier Inc. All rights reserved.

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Year:  2013        PMID: 23395824     DOI: 10.1016/j.ab.2013.01.019

Source DB:  PubMed          Journal:  Anal Biochem        ISSN: 0003-2697            Impact factor:   3.365


  114 in total

1.  iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition.

Authors:  Hao Lin; En-Ze Deng; Hui Ding; Wei Chen; Kuo-Chen Chou
Journal:  Nucleic Acids Res       Date:  2014-10-31       Impact factor: 16.971

2.  Prediction of Protein-Protein Interaction Sites with Machine-Learning-Based Data-Cleaning and Post-Filtering Procedures.

Authors:  Guang-Hui Liu; Hong-Bin Shen; Dong-Jun Yu
Journal:  J Membr Biol       Date:  2015-11-12       Impact factor: 1.843

3.  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

4.  iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples.

Authors:  Muhammad Kabir; Maqsood Hayat
Journal:  Mol Genet Genomics       Date:  2015-08-30       Impact factor: 3.291

5.  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

6.  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

7.  Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations.

Authors:  Kahini Wadhawan; Inkit Padhi; Sebastian Gehrmann; Payel Das; Tom Sercu; Flaviu Cipcigan; Vijil Chenthamarakshan; Hendrik Strobelt; Cicero Dos Santos; Pin-Yu Chen; Yi Yan Yang; Jeremy P K Tan; James Hedrick; Jason Crain; Aleksandra Mojsilovic
Journal:  Nat Biomed Eng       Date:  2021-03-11       Impact factor: 25.671

8.  iAFP-Ense: An Ensemble Classifier for Identifying Antifreeze Protein by Incorporating Grey Model and PSSM into PseAAC.

Authors:  Xuan Xiao; Mengjuan Hui; Zi Liu
Journal:  J Membr Biol       Date:  2016-11-03       Impact factor: 1.843

Review 9.  Machine learning-enabled discovery and design of membrane-active peptides.

Authors:  Ernest Y Lee; Gerard C L Wong; Andrew L Ferguson
Journal:  Bioorg Med Chem       Date:  2017-07-08       Impact factor: 3.641

10.  Imbalanced multi-label learning for identifying antimicrobial peptides and their functional types.

Authors:  Weizhong Lin; Dong Xu
Journal:  Bioinformatics       Date:  2016-08-26       Impact factor: 6.937

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