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Cross and progressive information extraction-based biological clustering method

A progressive extraction and clustering method technology, applied in informatics, biostatistics, bioinformatics, etc., can solve problems such as poor accuracy, loss of useful information, and inability to determine the type of biological clustering

Active Publication Date: 2017-10-20
HEBEI UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a biological clustering method for cross-gradually extracting information to solve the problems of existing biological clustering methods such as loss of useful information, complicated process, poor accuracy, and inability to determine the type of biological clustering

Method used

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  • Cross and progressive information extraction-based biological clustering method
  • Cross and progressive information extraction-based biological clustering method
  • Cross and progressive information extraction-based biological clustering method

Examples

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Embodiment 1

[0117] Given 10 weavers and their 9 biological characteristics constitute the original biological information system (see Table 1), that is, G 10 ={1,2,3,4,5,6,7,8,9,10}, M 9 ={a,b,c,d,e,f,g,h,i}, where a represents body length, b represents wing length, c represents wing width, d represents pronotum length, e represents pronotum Plate height, f represents the femur length of the forefoot, g represents the femur length of the midfoot, h represents the femur length of the hind foot, and i represents the length of the egg-laying valve; Set G for biological individuals 10 and biometric set M 9 The binary relationship between them is shown in Table 1.

[0118] Table 1 Original biological information system

[0119]

[0120] According to step a, the original biological information system Perform preprocessing to obtain the purified biological information system (G 5 , M 6 , R 5×6 ):

[0121] According to step a1, the original bioinformatics system To perform bin...

Embodiment 2

[0206] In order to further illustrate the practicability of the present invention, the data source used in this embodiment is the Katydid Research Laboratory of Hebei University. The laboratory personnel collected 300 spinosa insects from Guangxi, Guizhou, Tibet, Yunnan, Hainan, Jiangxi, Hunan, Zhejiang, Taiwan, Chongqing and other regions as experimental materials. The present invention extracts 57 female weavers as biological individual collection G 57 , and the biological characteristics of 6 kinds of textile mothers as the biological characteristic set M 6 . The six biological characteristics are: pronotum length to body length ratio (a), forefoot femur length to body length ratio (b), midfoot femur length to body length ratio (c), hind femur length Ratio to body length (d), ratio of oviposition petal length to body length (e), ratio of wing length to wing width (f). A collection of biological individuals G 57 with biometric set M 6 original biological information sys...

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Abstract

The present invention provides a cross and progressive information extraction-based biological clustering method. The method comprises the following steps that: binarization and purification pre-processing is performed on an original biological information system, so that a purified biological information system can be obtained; biological clustering sets generated by sub-information systems are adopted as input, and are sequentially interpolated into biological individuals and biological features; biological features and biological individuals which are removed in the pre-processing are restored into the purified biological information system, so that an updated biological clustering set, a generated biological clustering set, an invariant biological clustering set and a biological clustering set which are generated by the original biological information system can be obtained. According to the method of the invention, the original biological information system is pre-processed, the upper edge and lower edge of each biological feature are reasonably set; the two factors, namely the biological individuals and the biological features are both considered; and therefore, complexity is reduced, the objective binarization biological information system can be obtained, the accuracy of clustering results is not required to be re-judged, and accurate biological clustering results can be directly obtained.

Description

technical field [0001] The invention relates to a biological clustering method, in particular to a biological clustering method for cross progressively extracting information. Background technique [0002] A biological information system is composed of a collection of biological individuals, a collection of biological characteristics and the relationship between the two collections. The system provides basic information for further biological analysis. Biological clustering is a means for people to express biological information. Each clustering result, that is, a concept, is composed of two parts, one part is the biological individual subset A, and the other part is the public biological feature B owned by the biological individual subset A. In addition, the relationship between A and B is: The sub-set of biological individuals with the common biological characteristic B must be A, and the public biological characteristic of the sub-set of biological individuals must be B...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06F19/24
CPCG16B40/00G06F18/232
Inventor 毛华蔺庚梅杨兰珍王刚刘祎超边迅
Owner HEBEI UNIVERSITY
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