A reinforcement learning framework for pooled oligonucleotide design.

ABSTRACT:

Motivation

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.

Results

We present a general reinforcement learning (RL) framework, called OligoRL, to solve oligo design problems with complex constraints. OligoRL allows 'black-box' design criteria and can be adapted to solve many oligo design problems. We highlight the flexibility of OligoRL by building tools to solve three distinct design problems: (i) finding po

SUBMITTER: David BM 

PROVIDER: S-EPMC9004649 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature