{"id":"W3129155125","doi":"10.1016/j.compbiomed.2021.104258","title":"FAD-BERT: Improved prediction of FAD binding sites using pre-training of deep bidirectional transformers","year":2021,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":58,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Ministry of Science and Technology, Taiwan","keywords":"Transformer; Computer science; Artificial intelligence; Training set; Training (meteorology); Machine learning; Electrical engineering; Engineering; Voltage; Physics","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":[0.0004390765,0.001287619,0.0008809273,0.0007638351,0.0003095433,0.0006455444,0.001676254,0.0007974314,0.005343504],"category_scores_gemma":[0.001152587,0.0003768379,0.0006199802,0.0004282968,0.0002501004,0.0008495669,0.0008870419,0.001175471,0.002171331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004766238,"about_ca_system_score_gemma":0.0009010007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007019213,"about_ca_topic_score_gemma":0.01270603,"domain_scores_codex":[0.9998765,0.00002269654,0.000005045776,0.00003983504,0.0000289666,0.00002701491],"domain_scores_gemma":[0.9997572,0.0001089819,0.00001398866,0.00003200502,0.00005679898,0.00003097864],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00318178,0.000660353,0.01047112,0.0006929432,0.0003215748,0.0005964916,0.000118562,0.3043858,0.06763969,0.01182216,0.04210875,0.5580007],"study_design_scores_gemma":[0.00005817125,0.00005635001,0.0004022152,0.00001231865,0.00002281021,0.0000490878,0.00001538599,0.9810369,0.01231087,0.004380461,0.001642205,0.00001315411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2341844,0.002494098,0.7152206,0.0005080035,0.0003444878,0.0001182386,0.005531599,0.03387952,0.007719168],"genre_scores_gemma":[0.8507274,0.0005092697,0.1347011,0.000221481,0.00005922038,0.00009179249,0.005530506,0.0006098233,0.007549378],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007019213,"threshold_uncertainty_score":0.01787579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01617659853623141,"score_gpt":0.3024081556868805,"score_spread":0.2862315571506491,"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."}}