{"version":"1.0","type":"rich","title":"Reproducibility of CRISPR-Cas9 Methods for generation of conditional mouse alleles: a multi-center evaluation","description":"This visualization uses a random forest classifier to analyze CRISPR-Cas9 experimental data from an Excel file, predicting whether guide and donor sequences yield correctly targeted mouse alleles. The code computes nucleotide and dinucleotide frequencies from sequence regions, then trains a 2000-tree RandomForestClassifier with balanced class weights using scikit-learn. It displays out-of-bag error rates, the top ten features ranked by importance, and confusion matrices from StratifiedKFold cross-validation, revealing which sequence features most strongly influence targeting success across multiple laboratory centers.","provider_name":"VizHub AI Studio","provider_url":"https://vizhub.com","html":"<iframe src=\"https://vizhub.com/viz/gist-932f526867f7f8139b8e8eae7c76e866?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}