{"id":"W2105389823","doi":"10.1186/gb-2009-10-3-r29","title":"TFCat: the curated catalog of mouse and human transcription factors","year":2009,"lang":"en","type":"article","venue":"Genome biology","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":213,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; McGill University and Génome Québec Innovation Centre; Child and Family Research Institute; McGill University; University of British Columbia","funders":"National Institute of Allergy and Infectious Diseases; Ontario Institute for Cancer Research; National Institutes of Health; Michael Smith Health Research BC; Canadian Institutes of Health Research; McGill University Health Centre; McGill University","keywords":"Computational biology; Transcription factor; Biology; Computer science; Information retrieval; Homology (biology); Function (biology); World Wide Web; Genetics; Gene","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":[0.0008545389,0.0023475,0.001936941,0.01056179,0.001382113,0.001714363,0.001819221,0.0009782023,0.03949992],"category_scores_gemma":[0.002193871,0.000731125,0.001122516,0.01087627,0.0003676607,0.001371051,0.001522376,0.001106201,0.03954963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000750447,"about_ca_system_score_gemma":0.003084786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005416175,"about_ca_topic_score_gemma":0.01124223,"domain_scores_codex":[0.9991207,0.00007355792,0.000143878,0.0002321297,0.0003074331,0.0001223193],"domain_scores_gemma":[0.9988432,0.0001581944,0.0002088308,0.0002411137,0.0003359423,0.000212663],"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":[0.001523554,0.0001026265,0.009566889,0.004589375,0.0002096118,0.000711124,0.0003089677,0.0007792184,0.1776208,0.006366802,0.6317334,0.1664875],"study_design_scores_gemma":[0.00007318777,0.00006197425,0.01252977,0.0002217517,0.0001467817,0.0009927714,0.000040431,0.0005408257,0.01165942,0.00164104,0.9720249,0.00006713514],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01881449,0.01156742,0.02622426,0.0003428481,0.0004423907,0.000266721,0.9011126,0.01871464,0.02251464],"genre_scores_gemma":[0.008336871,0.002658137,0.01987804,0.0001738622,0.00007566385,0.0003311149,0.9585922,0.001286994,0.008667041],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03949992,"threshold_uncertainty_score":0.1321403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009509502236702279,"score_gpt":0.2310902654413785,"score_spread":0.2215807632046762,"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."}}