{"id":"W2079067121","doi":"10.1038/ncomms6469","title":"Integrated Omic analysis of lung cancer reveals metabolism proteome signatures with prognostic impact","year":2014,"lang":"en","type":"article","venue":"Nature Communications","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":113,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Institute for Cancer Research; University of Toronto; Princess Margaret Cancer Centre; Hospital for Sick Children","funders":"Canadian Cancer Society Research Institute; Canadian Institutes of Health Research; Terry Fox Foundation; Canada Research Chairs; Princess Margaret Cancer Foundation; Cancer Care Ontario; Memorial Sloan-Kettering Cancer Center","keywords":"Proteome; Biology; Genome; Lung cancer; Phenotype; Gene; Computational biology; Disease; Genetics; Cancer; Bioinformatics; Cancer research; Medicine; Pathology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004336739,0.0005259204,0.0006807944,0.00225248,0.0002844539,0.001301583,0.0002576463,0.0003898903,0.001253487],"category_scores_gemma":[0.0008603221,0.0001599871,0.0006178251,0.002556863,0.0001516911,0.0004444043,0.0007130465,0.0003782129,0.0004277201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004432451,"about_ca_system_score_gemma":0.0005203856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001061843,"about_ca_topic_score_gemma":0.002274767,"domain_scores_codex":[0.9996852,0.00005322985,0.00002944129,0.00009169654,0.0000749048,0.00006556482],"domain_scores_gemma":[0.9995503,0.0001094648,0.0001409536,0.00006012703,0.00008081059,0.00005831002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003089976,0.0003433967,0.4872808,0.000772043,0.001567884,0.0008307213,0.0001735023,0.004653356,0.3653308,0.00104044,0.003719289,0.1311979],"study_design_scores_gemma":[0.00008755479,0.0004976768,0.8955866,0.0001346056,0.001339715,0.00144167,0.0003856344,0.03284753,0.05433221,0.005481869,0.007813973,0.00005108327],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9680936,0.006114616,0.007092693,0.001061563,0.00004598621,0.00003522343,0.0150602,0.0003004901,0.002195641],"genre_scores_gemma":[0.98418,0.001198715,0.004993854,0.0002095383,0.0000433055,0.00002403125,0.008898542,0.0000282881,0.0004238071],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00225248,"threshold_uncertainty_score":0.004193306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006386177323072497,"score_gpt":0.2876528385015044,"score_spread":0.2812666611784319,"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."}}