{"id":"W3112395311","doi":"10.1093/neuonc/noaa215.892","title":"TAMI-03. IDENTIFICATION OF NOVEL DRIVERS OF LUNG-TO-BRAIN METASTASIS THROUGH IN VIVO FUNCTIONAL GENOMICS","year":2020,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"CRISPR; Brain metastasis; Metastasis; Primary tumor; Lung cancer; In vivo; Cancer research; Functional genomics; Cancer; Medicine; Cas9; Biology; Genome; Computational biology; Gene; Genomics; Oncology; Internal medicine; Genetics","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":[0.0004568782,0.0004778374,0.0003699846,0.0004808634,0.0003016209,0.0007236704,0.000883295,0.0006488971,0.003940523],"category_scores_gemma":[0.0001920936,0.0002537489,0.0005318956,0.0003132659,0.0003730136,0.0002413053,0.0003806768,0.001480514,0.00185238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006668272,"about_ca_system_score_gemma":0.0004876658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001154311,"about_ca_topic_score_gemma":0.001871368,"domain_scores_codex":[0.9997315,0.00003616266,0.00002485226,0.00006437267,0.00008573234,0.00005731652],"domain_scores_gemma":[0.9998098,0.00003472576,0.00005915067,0.00002013827,0.00001989558,0.00005634822],"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":[0.0001005953,0.00006651242,0.0002066801,0.00005514689,0.00001152746,0.0000980276,0.00002295633,0.0002602572,0.9944389,0.0009527112,0.0005622926,0.003224439],"study_design_scores_gemma":[0.00008812227,0.0004692217,0.003433965,0.0000236484,0.00005433086,0.0007884167,0.00007210764,0.009101484,0.947624,0.0004726051,0.03784768,0.00002448897],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8256444,0.002556615,0.1282622,0.001889364,0.0005648653,0.001097946,0.01574857,0.004813273,0.01942284],"genre_scores_gemma":[0.9112352,0.001399004,0.04925451,0.0006571494,0.00003463936,0.0006927958,0.01237763,0.001025737,0.02332335],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003940523,"threshold_uncertainty_score":0.01318234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02125884097446389,"score_gpt":0.3050911775633286,"score_spread":0.2838323365888647,"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."}}