Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition

Abstract

International audienc

A network-QSAR model for prediction of genetic-component biomarkers in human colorectal cancer

“…In statistical prediction, three cross-validation methods are often used: subsampling test, independent dataset test, and jackknife test (Chou and Zhang, 1995). However, as demonstrated in (Chou and Shen, 2007), the jackknife test has the least arbitrariness and therefore has been increasingly and widely used to test various prediction methods (see, e.g., (Chen et al, 2008;Chen and Han, 2009;Chou and Shen, 2008a;Chou and Shen, 2008b;Chou and Shen, 2009;Ding et al, 2009a;Ding et al, 2009b;Du and Li, 2008;Georgiou et al, 2009;2008;Nanni and Lumini, 2009;Rezaei et al, 2008;Shi et al, 2008;Tian et al, 2008;Wang et al, 2008;Xiao et al, 2009b;Zeng et al, 2009;). In the jackknife or leave-one-out test each case in the database is predicted for the model constructed using all the cases except the one being predicted.…”

A Short Survey on Genetic Sequences, Chou’s Pseudo Amino Acid Composition and its Combination with Fuzzy Set Theory

“…A clustering analysis of the twenty amino acids based on several physical properties is presented in [121]. The properties employed in the analysis are the following: the number of codons that code the protein, molecular weight, hydrophobicity, the number of atoms of different type and the corresponding number of protons as well as the number of total protons.…”

“…Representative results are that of Ding et al [123] who employed fuzzy support vector machine for the prediction of protein structure classes with pseudo aminoacid composition, Shen et al [124] who used Fuzzy KNN for predicting membrane protein types from pseudo aminoacid composition, Hayat et al [125] who employed fuzzy Knearest neighbor algorithms based on Chou's pseudo aminoacid composition, Shen et al [126] who applied supervised fuzzy clustering to predict protein structural classes and Goergiou et al [121] who applied fuzzy clustering techniques to classify aminoacids based on Chou's aminoacid composition.…”

SySAP: a system-level predictor of deleterious single amino acid polymorphisms

“…In statistical prediction, the following three cross-validation methods are often used to examine a predictor for its effectiveness in practical application: independent dataset test, subsampling test, and jackknife test (Chou and Zhang, 1995). However, as elucidated in Chou and Shen (2008) and demonstrated by Eqs.28-32 of Chou (2011), among the three cross-validation methods, the jackknife test is deemed the least arbitrary (most objective) that can always yield a unique result for a given benchmark dataset, and hence has been increasingly used and widely recognized by investigators to examine the accuracy of various predictors (Georgiou et al, 2009;Zeng et al, 2009;Esmaeili et al, 2010;Mohabatkar, 2010;Qiu et al, 2010;Hu et al, 2011aHu et al, , 2011bHuang et al, 2011aHuang et al, , 2011bLin et al, 2011;Wang et al, 2011;Xiao et al, 2011). Accordingly, the jackknife test, also known as Leave-One-Out Cross-Validation (LOOCV) (Huang et al, 2008;Cai et al, 2010;Huang et al, 2009Huang et al, , 2010aHuang et al, , 2010b) was adopted here to examine the quality of the present predictor.…”