Literature DB >> 23536215

Some remarks on predicting multi-label attributes in molecular biosystems.

Kuo-Chen Chou1.   

Abstract

Many molecular biosystems and biomedical systems belong to the multi-label systems in which each of their constituent molecules possesses one or more than one function or feature, and hence needs one or more than one label to indicate its attribute(s). With the avalanche of biological sequences generated in the post genomic age, it is highly desirable to develop computational methods to timely and reliably identify their various kinds of attributes. Compared with the single-label systems, the multi-label systems are much more complicated and difficult to deal with. The current mini review focuses on the recent progresses in this area from both conceptual aspects and detailed mathematical formulations.

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Year:  2013        PMID: 23536215     DOI: 10.1039/c3mb25555g

Source DB:  PubMed          Journal:  Mol Biosyst        ISSN: 1742-2051


  89 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.  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

4.  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

5.  Prediction of protein subcellular localization by incorporating multiobjective PSO-based feature subset selection into the general form of Chou's PseAAC.

Authors:  Monalisa Mandal; Anirban Mukhopadhyay; Ujjwal Maulik
Journal:  Med Biol Eng Comput       Date:  2015-01-07       Impact factor: 2.602

6.  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

7.  iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC.

Authors:  Yaser Daanial Khan; Nouman Rasool; Waqar Hussain; Sher Afzal Khan; Kuo-Chen Chou
Journal:  Mol Biol Rep       Date:  2018-10-11       Impact factor: 2.316

8.  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

9.  In silico prediction of chemical subcellular localization via multi-classification methods.

Authors:  Hongbin Yang; Xiao Li; Yingchun Cai; Qin Wang; Weihua Li; Guixia Liu; Yun Tang
Journal:  Medchemcomm       Date:  2017-03-29       Impact factor: 3.597

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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