CRISPRpred(SEQ): a sequence-based method for sgRNA on target activity prediction using traditional machine learning.

ABSTRACT:

Background

The latest works on CRISPR genome editing tools mainly employs deep learning techniques. However, deep learning models lack explainability and they are harder to reproduce. We were motivated to build an accurate genome editing tool using sequence-based features and traditional machine learning that can compete with deep learning models.

Results

In this paper, we present CRISPRpred(SEQ), a method for sgRNA on-target activity prediction that leverages only traditional machine learning techniques and hand-crafted features extracted from sgRNA sequences. We compare the results of CRISPRpred(SEQ) with that of DeepCRISPR, the current state-of-the-art, which uses a deep learning pipeline. Despite using only traditional machine learning methods, we have been able to beat

SUBMITTER: Muhammad Rafid AH 

PROVIDER: S-EPMC7268231 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature