The CRISPR-Cas9 system has revolutionized the field of genome editing and promises the ability to probe genetic interactions at their origin and the opportunity to cure severe inherited diseases. This system identifies a specific site by the complementarity between the guide RNA and the DNA target sequence, which is less expensive and time-consuming compared with previous gene-editing tools, as well as more precise and scalable. Here, we propose a study on the use of machine learning approaches in order to effectively design and evaluate CRISPR guide RNA.
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