{"id":"W1484359629","doi":"10.1080/21628130.2015.1040618","title":"An integrative exploratory analysis of –omics data from the ICGC cancer genomes lung adenocarcinoma study","year":2014,"lang":"en","type":"article","venue":"Systems Biomedicine","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Institutes of Health","keywords":"Adenocarcinoma; Computational biology; Genome; Lung cancer; Biology; Omics; Copy-number variation; Cancer; Cluster analysis; Bioinformatics; Genetics; Gene; Computer science; Oncology; Medicine; Artificial intelligence","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":[0.001944442,0.0005722089,0.0007048129,0.001916855,0.0004822037,0.0007350039,0.0005684158,0.0003951791,0.001321353],"category_scores_gemma":[0.003673134,0.0001323887,0.001064241,0.003195541,0.0002009463,0.0001387645,0.0009493564,0.0004844285,0.000222179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006928701,"about_ca_system_score_gemma":0.001358822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03523889,"about_ca_topic_score_gemma":0.0430471,"domain_scores_codex":[0.9987732,0.0007047124,0.00004790492,0.0001886683,0.0001894633,0.00009615417],"domain_scores_gemma":[0.9985901,0.0005951885,0.0001386766,0.0002778661,0.0002365867,0.0001616635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004999844,0.001163832,0.806101,0.001064723,0.005270741,0.002931674,0.001296927,0.02129472,0.01672374,0.003230767,0.03623949,0.09968257],"study_design_scores_gemma":[0.000301858,0.0005591991,0.9485241,0.0001006545,0.001007045,0.0007555881,0.001238102,0.02314709,0.002606633,0.001258285,0.02042425,0.00007716057],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9245749,0.001387795,0.00642301,0.001222234,0.00003119108,0.0002770022,0.06455746,0.0002086508,0.001317799],"genre_scores_gemma":[0.8551325,0.0005063837,0.02719598,0.0003135754,0.00004598356,0.0003818636,0.1155461,0.00008095682,0.0007966359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03523889,"threshold_uncertainty_score":0.07006758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03504260524452668,"score_gpt":0.3300022420932548,"score_spread":0.2949596368487282,"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."}}