{"id":"W2040607104","doi":"10.1371/journal.pone.0013066","title":"Ontology-Based Meta-Analysis of Global Collections of High-Throughput Public Data","year":2010,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":373,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nexen (Canada)","funders":"National Institute of General Medical Sciences; National Institutes of Health","keywords":"Data science; Context (archaeology); Computer science; Data mining; Ontology; DNA microarray; Systems biology; Public domain; Computational biology; Biology; Genetics; Gene; Geography","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":[0.02351213,0.001955897,0.002308386,0.02051604,0.001500237,0.005135358,0.003509715,0.00115168,0.0008109752],"category_scores_gemma":[0.04944529,0.0006896841,0.007811781,0.01304246,0.001282936,0.004216441,0.003943593,0.002185459,0.0002651322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002632783,"about_ca_system_score_gemma":0.004680252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007701384,"about_ca_topic_score_gemma":0.0142377,"domain_scores_codex":[0.9845489,0.005357273,0.001707636,0.003881692,0.003998921,0.0005056381],"domain_scores_gemma":[0.9459103,0.03248569,0.005504566,0.01157556,0.003653841,0.0008700991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.001690774,0.001203704,0.3399364,0.006872135,0.04045177,0.002734608,0.002087035,0.1912581,0.03818341,0.03870242,0.01192577,0.3249539],"study_design_scores_gemma":[0.00026771,0.0007515337,0.1082031,0.0008539051,0.01301865,0.00167536,0.00234902,0.58475,0.02719946,0.2326028,0.02796158,0.0003668106],"study_design_candidate":"meta_analysis","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1553531,0.005988989,0.7905957,0.002630933,0.000218307,0.000670652,0.03460585,0.007790038,0.002146494],"genre_scores_gemma":[0.4906619,0.001942686,0.4501458,0.0006111754,0.0001862972,0.000923908,0.05453757,0.0005473109,0.0004434441],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02351213,"threshold_uncertainty_score":0.1243455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1786814345115131,"score_gpt":0.3160648088762007,"score_spread":0.1373833743646876,"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."}}