{"id":"W2998817176","doi":"10.29173/hsi240","title":"The Predictive Power of Omics: Clinical Applications","year":2017,"lang":"en","type":"article","venue":"Health Science Inquiry","topic":"Biotechnology and Related Fields","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Omics; Predictive power; Computational biology; Computer science; Data science; Biology; Bioinformatics; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03653216,0.001900653,0.002690756,0.00691023,0.0009411254,0.007674705,0.001978621,0.003184258,0.006643882],"category_scores_gemma":[0.1604289,0.0007244919,0.001549856,0.005119127,0.00836463,0.007903383,0.003394508,0.005604001,0.001020571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00120487,"about_ca_system_score_gemma":0.002876922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001402677,"about_ca_topic_score_gemma":0.001067287,"domain_scores_codex":[0.9838805,0.01205147,0.0006438715,0.001260853,0.00196765,0.0001956612],"domain_scores_gemma":[0.6697089,0.3048939,0.006793119,0.01217287,0.004773033,0.001658102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001061616,0.0004606101,0.2032593,0.004336891,0.003408831,0.0008891558,0.001345636,0.007836456,0.003175583,0.2130762,0.01968209,0.5414677],"study_design_scores_gemma":[0.00009511056,0.0002360429,0.02520819,0.001553242,0.0007575539,0.0006730172,0.0008484055,0.009370283,0.001127589,0.9383985,0.02164899,0.00008302038],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.09012854,0.3820999,0.2324216,0.2448982,0.005885083,0.000215612,0.003417926,0.0008181903,0.04011507],"genre_scores_gemma":[0.7971986,0.1022025,0.06034397,0.02363127,0.01376333,0.0001828185,0.0007037456,0.0001499058,0.001823922],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.03653216,"threshold_uncertainty_score":0.1932029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09174674006042939,"score_gpt":0.4682028423081815,"score_spread":0.3764561022477522,"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."}}