Article abstract

Advancement in Scientific and Engineering Research

Review Article | Published October 2019 | Volume 4, Issue 2, pp. 31-36.

doi: https://doi.org/10.33495/aser_v4i2.19.105

 

An insightful recollection since the birth of Gordon Life Science Institute about 17 years ago

 


 

 

Kuo-Chen Chou

 

Email Author

 

Gordon Life Science Institute, Boston, Massachusetts 02478, USA. | Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China.


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Citation: Chou  K (2019). An insightful recollection since the birth of Gordon Life Science Institute about 17 years ago. Adv. Sci. Eng. Res. 4(1): 17-30.

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 Abstract 


Gordon Life Science Institute is the first Internet Research Institute ever established in the world. Recollected in this minireview is its establishing and developing processes, as well as its philosophy and accomplishments.

Keywords  Reform and opening   free communication   Sweden   cradle   San Diego   Boston   door-opening   

 

 

Copyright © 2019 Author(s) retain the copyright of this article.

This article is published under the terms of the Creative Commons Attribution License 4.0

 

 

  References 

 

 Awais M, Hussain W, Khan YD, Rasool N, Khan SA (2019). iPhosH-PseAAC: Identify phosphohistidine sites in proteins by blending statistical moments and position relative features according to the Chou's 5-step rule and general pseudo amino acid composition. IEEE/ACM Trans. Comput. Biol. Bioinform doi:10.1109/TCBB.2019.2919025 (2019).

Cai YD, Feng KY, Lu WC (2006). Using LogitBoost classifier to predict protein structural classes. J. Theo. Biol. 238: 172-176.

Cao DS, Xu QS, Liang YZ (2013). propy: a tool to generate various modes of Chou's PseAAC. Bioinformatics 29: 960-962.

Cheng X, Lin WZ, Xiao X (2019). pLoc_bal-mAnimal: predict subcellular localization of animal proteins by balancing training dataset and PseAAC. Bioinformatics 35: 398-406.

Cheng X, Xiao X (2017). pLoc-mPlant: predict subcellular localization of multi-location plant proteins via incorporating the optimal GO information into general PseAAC. Mol. BioSystems 13:1722-1727.

Cheng X, Xiao X (2017). pLoc-mVirus: predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC. Gene (Erratum: ibid., 2018, Vol. 644, 156-156) 628 (2017) 315-321.

Cheng X, Xiao, X (2018). pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC. Genomics 110: 50-58.

Cheng X, Zhao SG, Lin WZ, Xiao X (2017). pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites. Bioinformatics 33:3524-3531.

Chou KC (1983). Low-frequency vibrations of helical structures in protein molecules. Biochem. J. 209: 573-580.

Chou KC (1996). Review: Prediction of human immunodeficiency virus protease cleavage sites in proteins. Anal. Biochem. 233:1-14.

Chou KC (2001). Prediction of protein cellular attributes using pseudo amino acid composition. PROTEINS: Structure, Function, and Genetics (Erratum: ibid., 2001, Vol. 44, 60) 43 (2001) 246-255.

Chou KC (2005). Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 21: 10-19.

Chou KC (2009). Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Current Proteomics 6:262-274.

Chou KC (2011). Some remarks on protein attribute prediction and pseudo amino acid composition (50th Anniversary Year Review, 5-steps rule). J. Theo. Biol. 273: 236-247.

Chou KC (2015). Impacts of bioinformatics to medicinal chemistry. Medicinal Chem. 11: 218-234. Chou KC (2019). Progresses in predicting post-translational modification. Int. J. Peptide Res. Therapeut. DOI: 10.1007/s10989-019-09893-5.

Chou KC (2019). Recent progresses in predicting protein subcellular localization with artificial intelligence tools developed via the 5-steps rule. Medicinal Chemistry Submitted.

Chou KC, Cai YD (2003). Prediction and classification of protein subcellular location: sequence-order effect and pseudo amino acid composition. J. Cell. Biochem. (Addendum, ibid. 2004, 91, 1085) 90 (2003) 1250-1260.

Chou KC, Cheng X, Xiao X (2019). pLoc_bal-mEuk: predict subcellular localization of eukaryotic proteins by general PseAAC and quasi-balancing training dataset. Med. Chem. 15 (2019): 472-485.

Chou KC, Elrod DW (2002). Bioinformatical analysis of G-protein-coupled receptors. J. Proteome Res. 1:429-433.

Chou KC, Forsen S (1980). Graphical rules for enzyme-catalyzed rate laws. Biochem. J. 187: 829-835.

Chou KC, Forsen SU(1981). Graphical rules of steady-state reaction systems. Canadian J. Chem. 59: 737-755.

Ding H, Luo L, Lin H (2009). Prediction of cell wall lytic enzymes using Chou's amphiphilic pseudo amino acid composition. Protein & Peptide Letters 16: 351-355.

Du P, Gu S, Jiao Y (2014). PseAAC-General: Fast building various modes of general form of Chou's pseudo amino acid composition for large-scale protein datasets. Int. J. Molecular Sci. 15: 3495-3506.

Du P, Wang X, Xu C, Gao Y (2012). PseAAC-Builder: A cross-platform stand-alone program for generating various special Chou's pseudo amino acid compositions. Anal. Biochem. 425: 117-119.

Feng P, Yang H, Ding H, Lin H, Chen W (2019). iDNA6mA- PseKNC: Identifying DNA N(6)-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. Genomics 111: 96-102.

Gao QB, Jin ZC, Ye ZF, Wu C, He J (2009). Prediction of nuclear receptors with optimal pseudo amino acid composition. Anal. Biochem. 387: 54-59.

Georgiou DN, Karakasidis TE, Nieto JJ, Torres A (2009). Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition. J. Theo. Biol. 257: 17-26.

Ghauri AW, Khan YD, Rasool N, Khan SA (2018). pNitro-Tyr-PseAAC: Predict nitrotyrosine sites in proteins by incorporating five features into Chou's general PseAAC. Curr. Pharm. Des. 24: 4034-4043.

Guo ZM (2002). Prediction of membrane protein types by using pattern recognition method based on pseudo amino acid composition. Master Thesis, Bio-X Life Science Research Center, Shanghai Jiaotong University.

Hu L, Huang T, Shi X, Lu WC, Cai, YD (2011). Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties PLoS ONE 6: e14556.

Hussain W, Khan SD, Rasool N, Khan SA (2019). SPalmitoylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins. Anal. Biochem. 568:14-23.

Jia J, Liu Z, Xiao X (2015). iPPI-Esml: an ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC. J. Theo. Biol. 377: 47-56.

Jia J, Liu Z, Xiao X (2016). Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition (iPPBS-PseAAC). J. Biomol. Struct. Dyn. (JBSD) 34: 1946-1961.

Kabir M, Ahmad SA, Iqbal M, Hayat M (2019). iNR-2L: A two-level sequence-based predictor developed via Chou's 5-steps rule and general PseAAC for identifying nuclear receptors and their families. Genomics doi:10.1016/j.ygeno.2019.02.006.

Li ZC, Zhou XB, Dai Z, Zou XY (2009). Prediction of protein structural classes by Chou's pseudo amino acid composition: approached using continuous wavelet transform and principal component analysis. Amino Acids 37 (2009) 415-425.

Lin H, Deng EZ, Ding H, Chen W (2014). iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. Nucleic Acids Res. 42:12961-12972.

Liu B, Fang L, Long R, Lan X (2016). iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition. Bioinformatics 32: 362-369.

Ning Q, Ma Z, Zhao X (2019). dForml(KNN)-PseAAC: Detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou's 5-step rule and pseudo components. J. Theor. Biol. 470: 43-49.

Shen HB (2008). HIVcleave: a web-server for predicting HIV protease cleavage sites in proteins. Analytical Biochem. 375: 388-390. Shen HB (2008). PseAAC: a flexible web-server for generating various kinds of protein pseudo amino acid composition. Anal. Biochem. 373:386-388.

Wang L, Zhang R, Mu Y (2019). Fu-SulfPred: Identification of Protein S-sulfenylation Sites by Fusing Forests via Chou's General PseAAC. J Theor. Biol. 461: 51-58.

Xiao X, Cheng X, Chen G, Mao Q (2018). pLoc_bal-mVirus: Predict Subcellular Localization of Multi-Label Virus Proteins by Chou's General PseAAC and IHTS Treatment to Balance Training Dataset. Med. Chem 15: 496-509.

Xiao X, Cheng X, Chen G, Mao Q (2019). pLoc_bal-mGpos: predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC. Genomics 111: 886-892.

Xiao X, Cheng X, Su S, Nao Q (2017). pLoc-mGpos: Incorporate key gene ontology information into general PseAAC for predicting subcellular localization of Gram-positive bacterial proteins. Natural Sci. 9: 331-349.

Zhai X, Chen M, Lu W (2018). Accelerated search for perovskite materials with higher Curie temperature based on the machine learning methods. Computational Materials Sci. 151: 41-48.

Zhang CT (1992). An optimization approach to predicting protein structural class from amino acid composition. Protein Science 1:401-408.

Zhang Y, Xie R, Wang J, Leier A, Marquez-Lago TT, Akutsu T, Webb GI, Song J (2018). Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework. Brief in Bioinform doi: 10.1093/bib/bby079.