Background: The latest works on CRISPR genome editing tools mainly employs deep\nlearning techniques. However, deep learning models lack explainability and they are\nharder to reproduce. We were motivated to build an accurate genome editing tool\nusing sequence-based features and traditional machine learning that can compete\nwith deep learning models.\nResults: In this paper, we present CRISPRpred(SEQ), a method for sgRNA on-target\nactivity prediction that leverages only traditional machine learning techniques and\nhand-crafted features extracted from sgRNA sequences. We compare the results of\nCRISPRpred(SEQ) with that of DeepCRISPR, the current state-of-the-art, which uses a\ndeep learning pipeline. Despite using only traditional machine learning methods, we\nhave been able to beat DeepCRISPR for the three out of four cell lines in the benchmark\ndataset convincingly (2.174%, 6.905% and 8.119% improvement for the three cell lines).\nConclusion: CRISPRpred(SEQ) has been able to convincingly beat DeepCRISPR in 3 out\nof 4 cell lines. We believe that by exploring further, one can design better features only\nusing the sgRNA sequences and can come up with a better method leveraging only\ntraditional machine learning algorithms that can fully beat the deep learning models.
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