{"id":"W4311134061","doi":"10.1186/s12859-022-04582-5","title":"Boosting tissue-specific prediction of active cis-regulatory regions through deep learning and Bayesian optimization techniques","year":2022,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Charité – Universitätsmedizin Berlin; Berlin Institute of Health; Università degli Studi di Milano","keywords":"Artificial intelligence; Machine learning; Bayesian optimization; Deep learning; Computer science; Enhancer; Computational biology; Convolutional neural network; Epigenomics; Artificial neural network; Biology; DNA methylation; Transcription factor; Genetics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001451596,0.0001108359,0.0001161885,0.00004871819,0.0002660979,0.00001839169,0.00009900405,0.00008461051,0.0000172213],"category_scores_gemma":[0.00002733699,0.0001232107,0.00003794062,0.00009552763,0.00006581387,0.00001429417,0.0001992726,0.0001176521,3.088178e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003442541,"about_ca_system_score_gemma":0.00003831736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003408893,"about_ca_topic_score_gemma":0.000003270536,"domain_scores_codex":[0.9992599,0.00003945648,0.0003124779,0.000126231,0.0001292133,0.0001327251],"domain_scores_gemma":[0.9994506,0.00001371988,0.0002692982,0.0001768612,0.00005829996,0.00003118412],"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":[0.0003920694,0.0003256633,0.00739334,0.0009307429,0.0002538463,0.000002734506,0.01130943,0.7205815,0.1452117,0.005000941,0.002055784,0.1065423],"study_design_scores_gemma":[0.0005595392,0.0008489119,0.001011328,0.00003318719,0.00004106124,0.00007838103,0.007934986,0.9317311,0.03061297,0.000279437,0.0265351,0.0003339808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06163679,0.0003789864,0.934845,0.00001621882,0.0000798437,0.0002990528,0.00006268379,0.00004126611,0.002640158],"genre_scores_gemma":[0.5494185,0.0007494989,0.4487024,0.00004414479,0.00007768924,0.0000451098,0.0007537089,0.00003298038,0.0001759288],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4877817,"threshold_uncertainty_score":0.5024388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009196831369433146,"score_gpt":0.2200819021844075,"score_spread":0.2108850708149744,"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."}}