{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004333162,0.001213834,0.001440185,0.001005769,0.0008902166,0.001543393,0.001749825,0.001476127,0.001907542],"category_scores_gemma":[0.01615438,0.0005941596,0.001186605,0.0009229937,0.0006716634,0.001635456,0.001495684,0.001503981,0.000958964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009231903,"about_ca_system_score_gemma":0.001347607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003842259,"about_ca_topic_score_gemma":0.004197079,"domain_scores_codex":[0.9974895,0.001010918,0.0001410155,0.0006044899,0.0005475173,0.0002066422],"domain_scores_gemma":[0.9889043,0.008499587,0.0004232347,0.0009406364,0.001014836,0.000217345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002794029,0.001506887,0.08026169,0.001439533,0.0007994102,0.000984099,0.0006296912,0.5422181,0.1296077,0.004189073,0.009927614,0.2256421],"study_design_scores_gemma":[0.00004387505,0.00009480971,0.002744005,0.000009960397,0.00003650286,0.0001455411,0.00004272899,0.9798579,0.01521507,0.001058743,0.0007302912,0.00002060163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6778835,0.0007804887,0.3028048,0.0004932539,0.00009416165,0.0002697397,0.001003842,0.01368156,0.002988669],"genre_scores_gemma":[0.8105049,0.0001593983,0.1860743,0.0002226584,0.00002718825,0.000190671,0.001856083,0.0004265294,0.0005383185],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.004333162,"threshold_uncertainty_score":0.02291626,"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."}}