{"id":"W2991567593","doi":"10.1038/s41467-019-13460-3","title":"Genomic and immune profiling of pre-invasive lung adenocarcinoma","year":2019,"lang":"en","type":"article","venue":"Nature Communications","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":277,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; National Human Genome Research Institute; Canadian Institutes of Health Research; National Natural Science Foundation of China; Shanghai Shen Kang Hospital Development Center; LUNGevity Foundation; Shanghai Municipal Health Commission; Stand Up To Cancer; Fudan University","keywords":"Immune system; Profiling (computer programming); Adenocarcinoma; Computational biology; Biology; Gene expression profiling; Genetics; Gene; Computer science; Gene expression; Cancer","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008265873,0.00007641065,0.00010622,0.00002989919,0.00005561387,0.00001067817,0.0004083878,0.0001728857,0.000004831801],"category_scores_gemma":[0.0001007493,0.00008012523,0.00004529152,0.00005256733,0.00007212883,0.00000217201,0.0004694582,0.0002021778,0.00000249046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001366942,"about_ca_system_score_gemma":0.0001012249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002006621,"about_ca_topic_score_gemma":0.00008154596,"domain_scores_codex":[0.9995365,0.00002846037,0.0001526639,0.0001472118,0.00004335497,0.00009185755],"domain_scores_gemma":[0.9985961,0.00007386637,0.00009166377,0.001113639,0.00009442117,0.00003035964],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00002997328,0.00003511475,0.1433545,0.00003382046,0.00005128691,1.163529e-7,0.00009037195,0.00003034539,0.8500224,0.00586122,0.000263789,0.0002269687],"study_design_scores_gemma":[0.000810447,0.0001942306,0.5776162,0.00004547173,0.00007042399,0.00001521048,0.0002140163,0.0005486297,0.4096674,0.0002663833,0.01027462,0.0002770434],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9262939,0.07137151,0.00004059701,0.0002606699,0.00008601209,0.0002674043,0.00005202625,0.000004484735,0.001623404],"genre_scores_gemma":[0.9904119,0.004630658,0.004460409,0.0001273004,0.00003667731,0.00001659092,0.0002081839,0.00001373369,0.00009452794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4403551,"threshold_uncertainty_score":0.3267412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007136673291031704,"score_gpt":0.2628319610852275,"score_spread":0.2556952877941958,"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."}}