{"id":"W3084673114","doi":"10.3390/cancers12092653","title":"Classifying Lung Neuroendocrine Neoplasms through MicroRNA Sequence Data Mining","year":2020,"lang":"en","type":"article","venue":"Cancers","topic":"Neuroendocrine Tumor Research Advances","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Weill Cornell Medical College; Génome Québec; McGill University","keywords":"microRNA; Neuroendocrine tumors; Small Cell Lung Carcinoma; Pathology; Biology; Lung; Pathological; Lung cancer; Computational biology; Small-cell carcinoma; Medicine; Internal medicine; Gene; Genetics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004494687,0.0002505146,0.0003906589,0.00005134008,0.0001224066,0.00005184485,0.0006826397,0.000006611082,0.0002404427],"category_scores_gemma":[0.0008198073,0.0002378908,0.0000644411,0.0005277943,0.0002136895,0.0005860498,0.0005313463,0.000515557,0.00005375736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004573261,"about_ca_system_score_gemma":0.0009003447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001667815,"about_ca_topic_score_gemma":0.00002922946,"domain_scores_codex":[0.9975427,0.00005829392,0.0003169769,0.0008660837,0.000488681,0.0007272756],"domain_scores_gemma":[0.9983481,0.0001377539,0.0001088648,0.0008761563,0.00007341348,0.0004556658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002259551,0.00006013785,0.04658124,0.002618363,0.0004052352,0.08082374,0.00136036,0.001645381,0.4367523,0.0002165479,0.4045911,0.02268599],"study_design_scores_gemma":[0.004821695,0.001009361,0.0007383167,0.0004514332,0.0002168093,0.005919,0.001446189,0.05710316,0.0742644,0.00002775834,0.8532821,0.0007197899],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9239078,0.004949855,0.00109989,0.05599825,0.0009947835,0.00147936,0.0003242812,0.0007556114,0.01049021],"genre_scores_gemma":[0.9731669,0.001373107,0.01084845,0.01297826,0.0006514554,0.00003741576,0.0001533636,0.00009730211,0.0006937039],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.448691,"threshold_uncertainty_score":0.9700906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1555561398641091,"score_gpt":0.3928839582758323,"score_spread":0.2373278184117232,"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."}}