{"id":"W1542595575","doi":"10.1186/1471-2105-7-270","title":"Improving the specificity of high-throughput ortholog prediction","year":2006,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":108,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; Simon Fraser University; University of British Columbia","funders":"Genome Prairie; Canadian Institutes of Health Research; Genome British Columbia; Michael Smith Health Research BC; Genome Canada","keywords":"Throughput; Computational biology; DNA microarray; Computer science; Biology; Bioinformatics; Genetics; Gene; Gene expression","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":[0.0001639759,0.0001018208,0.000107548,0.00001831692,0.00009576852,0.00001389178,0.0001423052,0.00008078865,0.000003518099],"category_scores_gemma":[0.00003048229,0.00007114401,0.00006736544,0.00005857847,0.0001183109,0.000001307855,0.0001223223,0.00004595412,0.000004795746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006792843,"about_ca_system_score_gemma":0.00003852241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001033275,"about_ca_topic_score_gemma":0.0000881713,"domain_scores_codex":[0.9993171,0.00001605618,0.0003279321,0.0000969586,0.00009546565,0.0001464938],"domain_scores_gemma":[0.999414,0.00001510772,0.0001751014,0.0003086585,0.00007153108,0.00001559453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002882287,0.0003936,0.2352351,0.00071377,0.0003017836,0.000001412563,0.0009765964,0.02864388,0.6705646,0.0131775,0.02696494,0.02273862],"study_design_scores_gemma":[0.002008581,0.000921745,0.5911167,0.00002892471,0.0001646543,0.00005567779,0.001601313,0.03186102,0.3249147,0.002043968,0.04458537,0.0006973607],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.935775,0.0004160751,0.06078007,0.00003097954,0.0002446376,0.000192164,0.0000965947,0.000005638934,0.00245885],"genre_scores_gemma":[0.9686918,0.00008678826,0.0306612,0.00004996683,0.0002496411,0.000007922584,0.00006107789,0.000008402769,0.0001831606],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3558817,"threshold_uncertainty_score":0.2901168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01027637910918326,"score_gpt":0.2008079853663313,"score_spread":0.1905316062571481,"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."}}