{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006149258,0.0007404329,0.0006373003,0.0005973433,0.0002697071,0.0007360679,0.0007334642,0.0007449761,0.0030827],"category_scores_gemma":[0.0002882534,0.0002653284,0.0006132536,0.0003957026,0.0003879383,0.0001772258,0.0003858908,0.0009956501,0.001107231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006495054,"about_ca_system_score_gemma":0.0006472082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001392144,"about_ca_topic_score_gemma":0.002460558,"domain_scores_codex":[0.9995133,0.00006856443,0.00004842871,0.0001161456,0.0001749035,0.00007873525],"domain_scores_gemma":[0.9996837,0.0000872352,0.00008472706,0.0000317615,0.00003618039,0.0000763511],"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":[0.00006706754,0.00007650244,0.0003382479,0.00006805429,0.00001555797,0.000106135,0.00001069125,0.0003787078,0.9959329,0.0002481687,0.0002516747,0.002506335],"study_design_scores_gemma":[0.00006224217,0.0007023804,0.002704534,0.00001626429,0.00007475417,0.0006902981,0.00004556972,0.004838295,0.9801329,0.0001638456,0.01054553,0.00002324918],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8834352,0.002401951,0.08194074,0.0009418231,0.0002259768,0.0009409532,0.01587697,0.002946549,0.0112898],"genre_scores_gemma":[0.922081,0.001680318,0.04444363,0.0005824757,0.00002262993,0.0004393802,0.01633665,0.0005449918,0.0138689],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0030827,"threshold_uncertainty_score":0.01031262,"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."}}