Open AccessA reinforcement learning framework for pooled oligonucleotide design
Author(s) -
Benjamin David,
Ryan M. Wyllie,
Ramdane Harouaka,
Paul A. Jensen
Publication year - 2022
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/btac073
Subject(s) - reinforcement learning , reinforcement , computer science , oligonucleotide , artificial intelligence , engineering , genetics , biology , structural engineering , dna
The goal of oligonucleotide (oligo) design is to select oligos that optimize a set of design criteria. Oligo design problems are combinatorial in nature and require computationally intensive models to evaluate design criteria. Even relatively small problems can be intractable for brute-force approaches that test every possible combination of oligos, so heuristic approaches must be used to find near-optimal solutions.