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

A technology of correlation analysis and correlation classification, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as research, no deep consideration of knowledge base, incompatibility, etc.

Inactive Publication Date: 2009-01-14
UNIV OF SCI & TECH BEIJING
View PDF2 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Secondary protein structure forecasting technique based on association analysis and association classification
  • Secondary protein structure forecasting technique based on association analysis and association classification
  • Secondary protein structure forecasting technique based on association analysis and association classification

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0031] 1. Theoretical basis:

[0032] 1. Knowledge representation method - language field and language value structure

[0033] Definition 1: C=, if the following conditions are met:

[0034] (1) D is the set of intersecting closed intervals on the domain R of the basic variable, and D+ is its corresponding open set;

[0035] (2) N≠Φ is a finite set of linguistic values;

[0036] (3) ≤ N is a total order relation on N;

[0037] (4) I: N→D is the standard value mapping, which satisfies the order preservation, that is: n1, n2∈N (n1≠n2∧n1≤N n2→I(n1)≤I(n2)), (≤ is a partial order relationship); then C is called a language field.

[0038] Definition 2: For language field C=, F= is said to be the language value structure of C, if: (1) C satisfies Definition 1;

[0039] (2) K is a natural number;

[0040] (3) W: N→Rk satisfies:

[0041] n1,n2∈N(n1≤N ​​n2→W(n1)≤dicW(n2)>,

[0042] n1, n2∈N (n1≠n2→W(n1)≠W(n2)).

[0043] Among them, ≤dic is the lexicographical order on [0,...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F19/00G06F17/30G06F19/24
Inventor 杨炳儒
Owner UNIV OF SCI & TECH BEIJING
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products