Unsupervised classification of noisy chromosomes | Zendy

Tony Y. T. Chan | Zendy

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Unsupervised classification of noisy chromosomes

Author(s) -

Tony Y. T. Chan

Publication year - 2001

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/17.5.438

Subject(s) - computer science , string (physics) , pattern recognition (psychology) , noise (video) , artificial intelligence , representation (politics) , function (biology) , chromosome , process (computing) , mathematics , biology , image (mathematics) , genetics , gene , politics , political science , law , mathematical physics , operating system

Almost all methods of chromosome recognition assume supervised training; i.e. we are given correctly classified chromosomes to start the training phase. Noise, if any, is confined only in the representation of the chromosomes and not in the classification of the chromosomes. During the recognition phase, the problem is simply to calculate the string edit distance of the unknowns to the representatives chosen from the training phase and classify the unknowns accordingly.

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