Biological sequence classification utilizing positive and unlabeled data | Zendy

Yuanyuan Xiao | Zendy; Mark R. Segal | Zendy

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Biological sequence classification utilizing positive and unlabeled data

Author(s) -

Yuanyuan Xiao,

Mark R. Segal

Publication year - 2008

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/btn089

Subject(s) - computer science , context (archaeology) , class (philosophy) , cluster analysis , set (abstract data type) , data mining , artificial intelligence , data set , sequence (biology) , identification (biology) , property (philosophy) , pattern recognition (psychology) , machine learning , paleontology , philosophy , genetics , botany , epistemology , biology , programming language

In the genomics setting, an increasingly common data configuration consists of a small set of sequences possessing a targeted property (positive instances) amongst a large set of sequences for which class membership is unknown (unlabeled instances). Traditional two-class classification methods do not effectively handle such data.

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