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Javad Arabpour / جواد عرب پور
Quantum Biology Provides New Insights into CRISPR-Cas9 sgRNA Efficiency With special thanks to Jaclyn M. Noshay, Tyler Walker, William G. Alexander, Dawn M. Klingeman, Jonathon Romero, Angelica M. Walker, Erica Prates, Carrie Eckert, Stephan Irle, David Kainer, and Daniel A. Jacobson for their contributions to this research. A new study published in Nucleic Acids Research has used quantum chemical calculations and machine learning to develop a new model for predicting the efficiency of CRISPR-Cas9 guide RNAs (sgRNAs). The model, which is based on explainable AI, provides insights into the underlying mechanisms of CRISPR-Cas9 and could be used to design more efficient sgRNAs for genome editing. Key Findings: Quantum Chemical Calculations: The study used quantum chemical calculations to model the interactions between sgRNAs and target DNA. This allowed the researchers to identify key features that are important for sgRNA efficiency. Machine Learning: The researchers used a machine learning model called iterative Random Forest (iRF) to identify the most important features for predicting sgRNA efficiency. The model was able to accurately predict the efficiency of sgRNAs in both E. coli and H. sapiens. Explainable AI: The model is based on explainable AI, which means that the researchers can understand how the model makes its predictions. This is important for understanding the underlying mechanisms of CRISPR-Cas9. New Insights: The study provides new insights into the role of quantum mechanics in CRISPR-Cas9 sgRNA efficiency. The findings could be used to design more efficient sgRNAs for genome editing. Read the full research article here: https://lnkd.in/dUZmzEPp #CRISPR #GeneEditing #QuantumBiology #Bioinformatics #MachineLearning #ExplainableAI #GenomeEditing #DrugDiscovery #Biotechnology #FutureofMedicine #Research
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