{"id":"W2101456936","doi":"10.1093/bioinformatics/btn645","title":"Predicting the binding preference of transcription factors to individual DNA <i>k</i>-mers","year":2008,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Howard Hughes Medical Institute","keywords":"Inference; Computational biology; Biology; Transcription factor; DNA; DNA sequencing; DNA binding site; Preference; Gene; Genetics; Mechanism (biology); Computer science; Gene expression; Artificial intelligence; Mathematics; Promoter","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":[0.0008648748,0.0006178336,0.0008209586,0.00126911,0.0003090723,0.0004778404,0.0005610989,0.0007397882,0.001043904],"category_scores_gemma":[0.002559728,0.0002043773,0.0006782561,0.0008607789,0.0002368664,0.0004970682,0.000243359,0.0005527104,0.0008559077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004595242,"about_ca_system_score_gemma":0.0004251056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003853736,"about_ca_topic_score_gemma":0.006498854,"domain_scores_codex":[0.9996402,0.00006000206,0.00003329492,0.0001759092,0.00004959472,0.00004104643],"domain_scores_gemma":[0.998619,0.0008586932,0.0001726629,0.00009448513,0.0001526408,0.0001025824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00422148,0.0006274263,0.4186376,0.00112015,0.0005690409,0.0003473609,0.0001804152,0.2372771,0.2054892,0.0009433724,0.005889475,0.1246973],"study_design_scores_gemma":[0.00008812499,0.00027236,0.1025818,0.00002543393,0.0001351039,0.0006115602,0.0001056159,0.837896,0.05348423,0.002546547,0.002204689,0.00004846706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9542553,0.0004572307,0.03834361,0.00009892334,0.000009459404,0.00003517284,0.00544989,0.000801035,0.0005494324],"genre_scores_gemma":[0.9354697,0.0001707267,0.04940554,0.00004830558,0.0000139003,0.00004057173,0.01434379,0.00007130658,0.0004361504],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003853736,"threshold_uncertainty_score":0.007662594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02841896909652167,"score_gpt":0.2170266167777433,"score_spread":0.1886076476812216,"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."}}