{"id":"W3215884701","doi":"10.1093/neuonc/noab196.863","title":"TMOD-01. AN IN VIVO FUNCTIONAL GENOMICS SCREEN TO IDENTIFY NOVEL DRIVERS OF LUNG-TO-BRAIN METASTASIS","year":2021,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Chemical Reactions and Isotopes","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"In vivo; Lung cancer; Brain metastasis; Metastasis; CRISPR; Lung; Cancer; Primary tumor; Medicine; Brain tumor; Cancer research; Biology; Computational biology; Bioinformatics; Neuroscience; Pathology; Gene; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005157938,0.0002166366,0.0004762363,0.0002584374,0.0001076457,0.00001616173,0.0002875373,0.0003805348,0.003427782],"category_scores_gemma":[0.0006000753,0.0002421877,0.0001391855,0.0006253352,0.0001278326,0.0001431026,0.0002653459,0.0007512257,0.00009753199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003574349,"about_ca_system_score_gemma":0.0004292146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000790177,"about_ca_topic_score_gemma":0.0004771806,"domain_scores_codex":[0.9978942,0.0003925032,0.0005172605,0.0005673767,0.0001701903,0.0004584329],"domain_scores_gemma":[0.9978145,0.001127667,0.0001278007,0.0002907611,0.0001951291,0.0004440844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005048307,0.000943598,0.002724134,0.00001553239,0.0001085382,0.0001004813,0.0004440956,0.002258936,0.9602725,0.0009403287,0.02298168,0.008705345],"study_design_scores_gemma":[0.001418352,0.0003528683,0.003241294,0.000004177138,0.00009565877,0.00006539362,0.0002216035,0.0006529057,0.4165326,0.00008597515,0.5771602,0.0001690172],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9855178,0.00005657846,0.00120763,0.006656124,0.00136263,0.0004195843,0.0003236737,0.00004084895,0.004415102],"genre_scores_gemma":[0.9757429,0.00009562273,0.002927754,0.02033107,0.0003478619,0.00009405949,0.00007944048,0.00004329696,0.0003380416],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5541785,"threshold_uncertainty_score":0.9974832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.125865337570905,"score_gpt":0.4465192405110154,"score_spread":0.3206539029401104,"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."}}