Domain-specific introduction to machine learning terminology, pitfalls and opportunities in CRISPR-based gene editing.

Domain-specific introduction to machine learning terminology, pitfalls and opportunities in CRISPR-based gene editing.
Brief Bioinform. 2020 Feb 02;:
Authors: O'Brien AR, Burgio G, Bauer DC
Abstract
The use of machine learning (ML) has become prevalent in the genome engineering space, with applications ranging from predicting target site efficiency to forecasting the outcome of repair events. However, jargon and ML-specific accuracy measures have made it hard to assess the validity of individual approaches, potentially leading to misinterpretation of ML results. This review aims to close the gap by discussing ML approaches and pitfalls in the context of CRISPR gene-editing applications. Specifically, we address common considerations, such as algorithm choice, as well as problems, such as overestimating accuracy and data interoperability, by providing tangible examples from the genome-engineering domain. Equipping researchers with the knowledge to effectively use ML to better design gene-editing experiments and predict experimental outcomes will help advance the field more rapidly.
PMID: 32008042 [PubMed - as supplied by publisher]

Source: Briefings in Bioinformatics - February 1, 2020 Category: Bioinformatics Authors: O'Brien AR, Burgio G, Bauer DC Tags: Brief Bioinform Source Type: research