{"id":"W4313445110","doi":"10.1007/978-1-0716-2914-7_24","title":"A CRISPR Platform for Targeted In Vivo Screens","year":2023,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Saskatchewan Cancer Agency; University of Saskatchewan","funders":"","keywords":"CRISPR; Computational biology; Tumor microenvironment; Genome editing; In vivo; Genetic screen; In silico; Function (biology); Cancer; Biology; Bioinformatics; Computer science; Phenotype; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001296526,0.0015856,0.001695255,0.001972897,0.0009321878,0.00186498,0.002584294,0.001981863,0.009512107],"category_scores_gemma":[0.001024122,0.001851255,0.001319498,0.0008420889,0.0006945434,0.0007854927,0.002047599,0.004749762,0.01275554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007253039,"about_ca_system_score_gemma":0.0006775461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007071455,"about_ca_topic_score_gemma":0.001814815,"domain_scores_codex":[0.9974262,0.0003609125,0.0002349565,0.0004299991,0.001290455,0.0002574087],"domain_scores_gemma":[0.9990731,0.0002396272,0.0001380725,0.0003208916,0.00009175715,0.0001365519],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001086542,0.00008689536,0.00008955255,0.0001326483,0.00004037844,0.000153733,0.00003119067,0.0005790832,0.9720914,0.002524672,0.004612765,0.01954903],"study_design_scores_gemma":[0.0000744938,0.0001788365,0.0005553722,0.0000359777,0.00006377562,0.0009848358,0.00001544075,0.004091055,0.926722,0.001360224,0.06584588,0.00007216573],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03310414,0.001942533,0.9020854,0.0008190735,0.0007241325,0.001138618,0.008851547,0.03546441,0.01587007],"genre_scores_gemma":[0.207832,0.004217943,0.699871,0.001056919,0.0001955477,0.002295597,0.02040593,0.007468337,0.05665667],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009512107,"threshold_uncertainty_score":0.03182113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02413438058395476,"score_gpt":0.4412596966195583,"score_spread":0.4171253160356035,"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."}}