Secondary protein structure forecasting technique based on association analysis and association classification

Abstract

The invention discloses a protein secondary structure prediction technology based on correlation analysis and correlation classification, wherein, based on a double-base cooperating mechanism, a KDD process model is introduced into the problem of protein secondary structure prediction; in a KAAPRO method, data mining (knowledge discovery) is used as a main body and Maradbcm arithmetic based on the KDD process model and a D-CBA method of correlation rule classification are adopted. The correlation rule obtained by the KAAPRO method discloses the influence relation of amino acid physical-chemical properties on the protein secondary structure, thus enhancing the precision of prediction. The characteristic of the Maradbcm arithmetic on mining accident rules mines the correlation rules of alpha protein base and beta protein base which have relatively high purity, therefore, the obtained mining results are the distillated rules. The D-CBA correlation classification method uses the measure of credibility and supportability as a composite measure for carrying out the protein correlation classification. While guaranteeing the prediction precision, the technology provides a basis for the further analysis of the secondary structure for biologists.

Description

[0001] The invention relates to protein secondary structure prediction technology, in particular to a KDD-based * The intelligent prediction technology of the process model, specifically a protein secondary structure prediction technology based on association analysis and association classification (KAAPRO). Background technique [0002] 1. Protein structure prediction technology: [0003] Protein structure prediction is an important task in the post-genomic era. Research on protein structure can be traced back to the 1970s. In the field of bioinformatics, researchers use information technology and mathematical methods to analyze biological macromolecules (DNA and protein) to understand the biological significance of biological macromolecules. The problem of protein structure prediction occupies an important position in many research contents of bioinformatics. [0004] As far as the basic methods of protein structure research are concerned, X-ray crystallography and multid...

AI Technical Summary

Problems solved by technology

At present, most KDD algorithms do not study KDD as a complex system of cognition and its internal regularity, and do not consider the knowledge base in depth. Duplicated and redundant, even incompatible, and relying only on human-computer interaction to form a focus, without reflecting the cognitive autonomy of the system itself, so the novelty and effectiveness required in the definition of KDD cannot be reflected

Images