{"id":"W4252669986","doi":"10.1201/9780849359507-25","title":"Introduction to Applied Bioinformatics","year":2005,"lang":"en","type":"book-chapter","venue":"Pharmacogenomics","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Computational biology; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.004315555,0.001953564,0.001529908,0.00331953,0.0009673859,0.005912131,0.003089956,0.001852385,0.08721734],"category_scores_gemma":[0.01654637,0.001517816,0.002343033,0.006205225,0.001326711,0.005256542,0.002529009,0.005886136,0.08729454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001245505,"about_ca_system_score_gemma":0.002539406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001747936,"about_ca_topic_score_gemma":0.002152676,"domain_scores_codex":[0.9952654,0.001582398,0.0005396023,0.0007322727,0.001724038,0.0001563971],"domain_scores_gemma":[0.9894781,0.006532161,0.0002030124,0.001522208,0.00193396,0.0003305688],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003803342,0.00006417702,0.0003147805,0.001159541,0.00008769191,0.0001925999,0.0001913144,0.003868394,0.001249725,0.06343748,0.4718698,0.4575264],"study_design_scores_gemma":[0.00002235762,0.00001802985,0.0003050949,0.0003558123,0.00001608466,0.0004571272,0.00005367985,0.007872002,0.0007829692,0.1535317,0.8365431,0.00004208312],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.0003768794,0.01303808,0.8901998,0.006066233,0.002405514,0.0004339784,0.007061043,0.02340757,0.05701097],"genre_scores_gemma":[0.006913587,0.02195958,0.8798724,0.005486695,0.002868375,0.001416772,0.01393551,0.007891051,0.05965588],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.08721734,"threshold_uncertainty_score":0.2917711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01866167880507079,"score_gpt":0.2773268582642182,"score_spread":0.2586651794591474,"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."}}