Literature DB >> 31894399

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

Kuo-Chen Chou1.   

Abstract

Facing the explosive growth of biological sequences unearthed in the post-genomic age, one of the most important but also most difficult problems in computational biology is how to express a biological sequence with a discrete model or a vector, but still keep it with considerable sequence-order information or its special pattern. To deal with such a challenging problem, the ideas of "pseudo amino acid components" and "pseudo K-tuple nucleotide composition" have been proposed. The ideas and their approaches have further stimulated the birth for "distorted key theory", "wenxing diagram", and substantially strengthening the power in treating the multi-label systems, as well as the establishment of the famous "5-steps rule". All these logic developments are quite natural that are very useful not only for theoretical scientists but also for experimental scientists in conducting genetics/genomics analysis and drug development. Presented in this review paper are also their future perspectives; i.e., their impacts will become even more significant and propounding.

Keywords:  5-Steps rule; Distorted key theory; Multi-label systems; PseAAC; PseKNC; Pseudo amino acid components; Wenxiang diagram

Mesh:

Year:  2020        PMID: 31894399     DOI: 10.1007/s00438-019-01634-z

Source DB:  PubMed          Journal:  Mol Genet Genomics        ISSN: 1617-4623            Impact factor:   3.291


  246 in total

1.  Predicting protein structural class by incorporating patterns of over-represented k-mers into the general form of Chou's PseAAC.

Authors:  Yu-Fang Qin; Chun-Hua Wang; Xiao-Qing Yu; Jie Zhu; Tai-Gang Liu; Xiao-Qi Zheng
Journal:  Protein Pept Lett       Date:  2012-04       Impact factor: 1.890

Review 2.  Recent progress in protein subcellular location prediction.

Authors:  Kuo-Chen Chou; Hong-Bin Shen
Journal:  Anal Biochem       Date:  2007-07-12       Impact factor: 3.365

3.  Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach.

Authors:  Yu-hong Zeng; Yan-zhi Guo; Rong-quan Xiao; Li Yang; Le-zheng Yu; Meng-long Li
Journal:  J Theor Biol       Date:  2009-03-31       Impact factor: 2.691

Review 4.  Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences.

Authors:  Wei Chen; Hao Lin; Kuo-Chen Chou
Journal:  Mol Biosyst       Date:  2015-10

5.  pLoc_bal-mVirus: Predict Subcellular Localization of Multi-Label Virus Proteins by Chou's General PseAAC and IHTS Treatment to Balance Training Dataset.

Authors:  Xuan Xiao; Xiang Cheng; Genqiang Chen; Qi Mao; Kuo-Chen Chou
Journal:  Med Chem       Date:  2019       Impact factor: 2.745

6.  iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins.

Authors:  Wei-Zhong Lin; Jian-An Fang; Xuan Xiao; Kuo-Chen Chou
Journal:  Mol Biosyst       Date:  2013-01-31

7.  Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou׳s general PseAAC.

Authors:  Abdollah Dehzangi; Rhys Heffernan; Alok Sharma; James Lyons; Kuldip Paliwal; Abdul Sattar
Journal:  J Theor Biol       Date:  2014-09-28       Impact factor: 2.691

8.  Predicting cleavability of peptide sequences by HIV protease via correlation-angle approach.

Authors:  J J Chou
Journal:  J Protein Chem       Date:  1993-06

9.  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

10.  PSNO: predicting cysteine S-nitrosylation sites by incorporating various sequence-derived features into the general form of Chou's PseAAC.

Authors:  Jian Zhang; Xiaowei Zhao; Pingping Sun; Zhiqiang Ma
Journal:  Int J Mol Sci       Date:  2014-06-25       Impact factor: 5.923

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