Classifying CRISPR-Cas9 Off-Target Cleavage Sites from GUIDE-seq Data: A Class-Imbalanced Machine Learning Benchmark
Off-target cleavage is a central safety concern for CRISPR-Cas9 genome editing, particularly in therapeutic applications where unintended double-strand breaks carry clinical risk. We benchmarked five machine learning classifiers: logistic regression on mismatch-count summary features, a random forest and a gradient boosting model on one-hot-encoded sgRNA/candidate-site sequence pairs, a…
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