Adaptive one-class Gaussian processes allow accurate prioritization of oncology drug targets | Zendy

Antonio de Falco | Zendy; Zoltán Dezső | Zendy; Francesco Ceccarelli | Zendy; Luigi Cerulo | Zendy; Angelo Ciaramella | Zendy; Michele Ceccarelli | Zendy

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Adaptive one-class Gaussian processes allow accurate prioritization of oncology drug targets

Author(s) -

Antonio de Falco,

Zoltán Dezső,

Francesco Ceccarelli,

Luigi Cerulo,

Angelo Ciaramella,

Michele Ceccarelli

Publication year - 2020

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/btaa968

Subject(s) - computer science , druggability , python (programming language) , prioritization , machine learning , source code , hyperparameter , benchmark (surveying) , class (philosophy) , selection (genetic algorithm) , drug development , set (abstract data type) , gaussian process , artificial intelligence , data mining , gaussian , drug , medicine , biology , pharmacology , programming language , biochemistry , physics , geodesy , management science , quantum mechanics , gene , economics , geography

The cost of drug development has dramatically increased in the last decades, with the number new drugs approved per billion US dollars spent on R&D halving every year or less. The selection and prioritization of targets is one the most influential decisions in drug discovery. Here we present a Gaussian Process model for the prioritization of drug targets cast as a problem of learning with only positive and unlabeled examples.

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