Naïve Bayes for microRNA target predictions—machine learning for microRNA targets | Zendy

Malik Yousef | Zendy; Segun Jung | Zendy; Andrew V. Kossenkov | Zendy; Louise C. Showe | Zendy; Michael K. Showe | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Naïve Bayes for microRNA target predictions—machine learning for microRNA targets

Author(s) -

Malik Yousef,

Segun Jung,

Andrew V. Kossenkov,

Louise C. Showe,

Michael K. Showe

Publication year - 2007

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btm484

Subject(s) - microrna , bayes' theorem , computer science , artificial intelligence , naive bayes classifier , machine learning , computational biology , bayesian probability , biology , support vector machine , genetics , gene

Most computational methodologies for miRNA:mRNA target gene prediction use the seed segment of the miRNA and require cross-species sequence conservation in this region of the mRNA target. Methods that do not rely on conservation generate numbers of predictions, which are too large to validate. We describe a target prediction method (NBmiRTar) that does not require sequence conservation, using instead, machine learning by a naïve Bayes classifier. It generates a model from sequence and miRNA:mRNA duplex information from validated targets and artificially generated negative examples. Both the 'seed' and 'out-seed' segments of the miRNA:mRNA duplex are used for target identification.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research