KAAPRO: An approach of protein secondary structure prediction based on KDD* in the compound pyramid prediction model
the problem of protein secondary structure prediction is one of the most important problems in Bioinformatics. After the study of this problem for 30 years and more, there have been some breakthroughs. Especially, the introduction of ensemble prediction model and hybrid prediction model makes the accuracy of prediction better, but there is a long distance to induce the tertiary structures from the secondary ones. As one of the extension researches of KDTICM [Bingru, Yang (2004). Knowledge discovery based oil theory of iron, cognition mechanism and application. Beijing: Electronic Industry Press] theory. this paper proposed a method KAAPRO, which is based oil Maradbem algorithm which is induced by KDD* model and combined with CBA, for protein secondary structure prediction. And a gradually enhanced, multi-layer systematic prediction model, compound pyramid model, is proposed. The kernel of this model is KAAPRO, Domain knowledge is used through the whole model, and the physical-chemical attributes are chosen by causal cellular automata. In the experiment, the test proteins used in reference Muggleton et al. (Muggleton, S. H., King, R., Sternberg, M. (1992). Protein secondary structure prediction using logic-based machine learning. Protein Engineering, 5(7), 647-657) are predicted. The structures of amino acids, whose structural trails arc obscure, are predicted well by KAAPRO. Hence, the result of this model is satisfying too. Crown Copyright (C) 2008 Published by Elsevier Ltd. All rights reserved.
protein secondary structure prediction kdd* compound pyramid model association analysis