{"id":"W4409727666","doi":"10.1101/2025.04.16.649183","title":"ChromeCRISPR - A High Efficacy Hybrid Machine Learning Model for CRISPR/Cas On-Target Predictions","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"CRISPR; Computer science; Artificial intelligence; Computational biology; Machine learning; Biology; Genetics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005445228,0.0009706782,0.0006731732,0.0004831391,0.0002795223,0.0005880654,0.001352998,0.001377475,0.002830961],"category_scores_gemma":[0.0009074585,0.0003340667,0.0007742366,0.00026602,0.0002889572,0.0004759814,0.0004244162,0.001372743,0.0006203933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009209935,"about_ca_system_score_gemma":0.0008763286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01693634,"about_ca_topic_score_gemma":0.01575014,"domain_scores_codex":[0.9998276,0.00003714645,0.00000891928,0.00006380316,0.0000314975,0.00003094487],"domain_scores_gemma":[0.9996035,0.000229566,0.00002749551,0.00001966259,0.00009622171,0.00002360962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001539537,0.0001297338,0.001606967,0.0000453786,0.00007723372,0.0001004206,0.00001199438,0.9568889,0.001816285,0.0008040396,0.002481222,0.03588391],"study_design_scores_gemma":[0.000003049208,0.00001142405,0.00006858754,0.000001808006,0.00000336339,0.000003732443,9.091988e-7,0.999403,0.00023743,0.0001827913,0.00008222697,0.0000018246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4047244,0.00418048,0.5615485,0.002629619,0.0004780928,0.0002306583,0.003246053,0.01190623,0.01105595],"genre_scores_gemma":[0.9331659,0.0003166713,0.05644367,0.0005686591,0.0000664021,0.0001491229,0.002210598,0.0001322686,0.006946709],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01693634,"threshold_uncertainty_score":0.03367549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009870080416592963,"score_gpt":0.261146004161246,"score_spread":0.251275923744653,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}