Evaluating bacterial gene-finding HMM structures as probabilistic logic programs | Zendy

Søren Mørk | Zendy; Ian Holmes | Zendy

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Evaluating bacterial gene-finding HMM structures as probabilistic logic programs

Author(s) -

Søren Mørk,

Ian Holmes

Publication year - 2012

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/btr698

Subject(s) - computer science , probabilistic logic , statistical model , hidden markov model , benchmark (surveying) , graphical model , machine learning , artificial intelligence , scripting language , python (programming language) , source code , gene prediction , data mining , theoretical computer science , programming language , genome , gene , biology , biochemistry , geodesy , geography

Probabilistic logic programming offers a powerful way to describe and evaluate structured statistical models. To investigate the practicality of probabilistic logic programming for structure learning in bioinformatics, we undertook a simplified bacterial gene-finding benchmark in PRISM, a probabilistic dialect of Prolog.

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