{"id":"W2563350344","doi":"10.1038/srep39294","title":"Identification of cancer risk lncRNAs and cancer risk pathways regulated by cancer risk lncRNAs based on genome sequencing data in human cancers","year":2016,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Cancer-related molecular mechanisms research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Agency of Canada","funders":"Natural Science Foundation of Heilongjiang Province; National Natural Science Foundation of China; Harbin Applied Technology Research and Development Project","keywords":"Cancer; Identification (biology); Genome; Computational biology; Human genome; Biology; Cancer genome sequencing; DNA sequencing; Bioinformatics; Genetics; Gene","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":[0.0009822433,0.0003820172,0.0005259722,0.001998127,0.0004032476,0.0007118349,0.0002252288,0.0003221443,0.0007879704],"category_scores_gemma":[0.002312451,0.0001173041,0.0009273792,0.002093102,0.0002231477,0.0005094383,0.0006678133,0.0003959355,0.0002481854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003538865,"about_ca_system_score_gemma":0.0008771999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001923937,"about_ca_topic_score_gemma":0.003795687,"domain_scores_codex":[0.9992483,0.0001307565,0.0001044444,0.0003108292,0.0001269996,0.00007857008],"domain_scores_gemma":[0.9988883,0.0004848961,0.0002678885,0.0001112777,0.000153905,0.00009381538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001088238,0.0002056863,0.7773666,0.001131131,0.001052053,0.0008398596,0.0003720486,0.008248132,0.1194322,0.0009734894,0.002682234,0.08660842],"study_design_scores_gemma":[0.00006256731,0.0003615984,0.898403,0.0001761352,0.001303321,0.001768874,0.0006366021,0.03692898,0.0373107,0.004719055,0.01821787,0.0001112972],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9614849,0.005701778,0.01190049,0.0004203465,0.00004437166,0.00007277469,0.01905724,0.0002399053,0.001078178],"genre_scores_gemma":[0.9520253,0.001671614,0.01172165,0.000180761,0.00002605401,0.0001002261,0.03383327,0.00004245135,0.0003987408],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001998127,"threshold_uncertainty_score":0.005194664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01918605225515802,"score_gpt":0.2972033707233158,"score_spread":0.2780173184681578,"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."}}