Amino Acid Principal Component Analysis (AAPCA) and its applications in protein structural class prediction

Journal of Biomolecular Structure & Dynamics
Qi-Shi DuKou-Chen Chou

Abstract

The extremely complicated nature of many biological problems makes them bear the features of fuzzy sets, such as with vague, imprecise, noisy, ambiguous, or input-missing information For instance, the current data in classifying protein structural classes are typically a fuzzy set To deal with this kind of problem, the AAPCA (Amino Acid Principal Component Analysis) approach was introduced. In the AAPCA approach the 20-dimensional amino acid composition space is reduced to an orthogonal space with fewer dimensions, and the original base functions are converted into a set of orthogonal and normalized base functions The advantage of such an approach is that it can minimize the random errors and redundant information in protein dataset through a principal component selection, remarkably improving the success rates in predicting protein structural classes It is anticipated that the AAPCA approach can be used to deal with many other classification problems in proteins as well.

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Citations

Apr 23, 2008·Amino Acids·Xing-Ming ZhaoKazuyuki Aihara
Apr 23, 2008·Amino Acids·Lukasz A KurganJishou Ruan
Apr 8, 2010·The Protein Journal·Zhonglong Zheng, Jie Yang
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Apr 22, 2008·Journal of Theoretical Biology·Chao ChenPei-Xiang Cai
Jun 26, 2007·Biochemical and Biophysical Research Communications·Kuo-Chen Chou, Hong-Bin Shen
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Oct 28, 2019·Current Topics in Medicinal Chemistry·Kuo-Chen Chou
Nov 30, 2019·Current Pharmaceutical Design·Kuo-Chen Chou
Nov 15, 2016·Physical Review. E·Pradeep Bhadola, Nivedita Deo

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