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Titolo:
Some insights into protein structural class prediction
Autore:
Zhou, GP; Assa-Munt, N;
Indirizzi:
Burnham Inst, Dept Biol Struct, La Jolla, CA 92037 USA Burnham Inst La Jolla CA USA 92037 pt Biol Struct, La Jolla, CA 92037 USA
Titolo Testata:
PROTEINS-STRUCTURE FUNCTION AND GENETICS
fascicolo: 1, volume: 44, anno: 2001,
pagine: 57 - 59
SICI:
0887-3585(20010701)44:1<57:SIIPSC>2.0.ZU;2-L
Fonte:
ISI
Lingua:
ENG
Soggetto:
AMINO-ACID-COMPOSITION;
Keywords:
protein structural class; amino acid composition; Mahalanobis distance; component-coupled algorithm; Bayes decision rule;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Life Sciences
Citazioni:
13
Recensione:
Indirizzi per estratti:
Indirizzo: Zhou, GP Burnham Inst, Dept Biol Struct, 10901 N Torrey Pines Rd, La Jolla, CA 92037 USA Burnham Inst 10901 N Torrey Pines Rd La Jolla CA USA 92037 37 USA
Citazione:
G.P. Zhou e N. Assa-Munt, "Some insights into protein structural class prediction", PROTEINS, 44(1), 2001, pp. 57-59

Abstract

It has been quite clear that the success rate for predicting protein structural class can be improved significantly by using the algorithms that incorporate the coupling effect among different amino acid components of a protein. However, there is still a lot of confusion in understanding the relationship of these advanced algorithms, such as the least Mahalanobis distancealgorithm, the component-coupled algorithm, and the Bayes decision rule. In this communication, a simple, rigorous derivation is provided to prove that the Bayes decision rule introduced recently for protein structural classprediction is completely the same as the earlier component-coupled algorithm. Meanwhile, it is also very clear from the derivative equations that theleast Mahalanobis distance algorithm is an approximation of the component-coupled algorithm, also named as the covariant-discriminant algorithm introduced by Chou and Elrod in protein subcellular location prediction (ProteinEngineering, 1999; 12:107-118), Clarification of the confusion will help use these powerful algorithms effectively and correctly interpret the results obtained by them, so as to conduce to the further development not only inthe structural prediction area, but in some other relevant areas in protein science as well. Proteins 2001;44:57-59, (C) 2001 Wiley-Liss,Inc.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 25/11/20 alle ore 14:11:06