{"id":"W2078801689","doi":"10.1016/j.clinbiochem.2004.07.010","title":"Venn analysis as part of a bioinformatic approach to prioritize expressed sequence tags from cardiac libraries","year":2004,"lang":"en","type":"article","venue":"Clinical Biochemistry","topic":"Congenital heart defects research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Actua; Western University; Robarts Clinical Trials","funders":"","keywords":"Venn diagram; Expressed sequence tag; Biology; Annotation; Computational biology; Sequence (biology); Gene; Genetics; Information retrieval; Genome; Computer science","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":[0.002501499,0.001248111,0.001625654,0.004415627,0.001395079,0.001853148,0.001509294,0.0007255103,0.004415709],"category_scores_gemma":[0.004922212,0.000518081,0.001826466,0.003410885,0.0004358414,0.001038844,0.0009172139,0.001786095,0.001444301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008748323,"about_ca_system_score_gemma":0.001524184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002151163,"about_ca_topic_score_gemma":0.00601575,"domain_scores_codex":[0.9985865,0.0003146013,0.0001305639,0.0004380128,0.0003875019,0.0001428242],"domain_scores_gemma":[0.9977072,0.00127552,0.0001986093,0.0001941833,0.0004152034,0.0002092029],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003746215,0.0008778576,0.02891392,0.004045811,0.001664409,0.0009935445,0.0008943653,0.01677667,0.6503127,0.009630309,0.017192,0.2649522],"study_design_scores_gemma":[0.000606073,0.001324836,0.05660326,0.0003139922,0.001420252,0.003420297,0.0007753611,0.5140265,0.3258723,0.02905468,0.06609356,0.0004887973],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2710098,0.001801732,0.6709414,0.0006904014,0.0003966436,0.0009445145,0.02485098,0.02542715,0.003937424],"genre_scores_gemma":[0.2089279,0.0005832353,0.7498186,0.0003301285,0.00008550873,0.0008476909,0.03399559,0.003066728,0.002344612],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004415709,"threshold_uncertainty_score":0.01477206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04008166319431064,"score_gpt":0.3474749767124803,"score_spread":0.3073933135181697,"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."}}