{"id":"W2965351431","doi":"10.1093/bioinformatics/btz593","title":"DEEPCON: protein contact prediction using dilated convolutional neural networks with dropout","year":2019,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ryerson University; University of Missouri; Nvidia; University of Missouri-St. Louis; National Science Foundation","keywords":"Computer science; Convolutional neural network; Dropout (neural networks); Artificial intelligence; Artificial neural network; Residual; Deep learning; Range (aeronautics); Machine learning; Pattern recognition (psychology); Algorithm","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001223173,0.00186662,0.0009508705,0.0007773401,0.0006535221,0.0009221701,0.002426972,0.001514838,0.003534398],"category_scores_gemma":[0.002317243,0.0005938327,0.000715575,0.000889938,0.0006032892,0.001914328,0.001355144,0.001487794,0.002064335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00172652,"about_ca_system_score_gemma":0.001598977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01260954,"about_ca_topic_score_gemma":0.01876303,"domain_scores_codex":[0.9994881,0.00007722206,0.00002120998,0.0001736766,0.0001633189,0.00007634788],"domain_scores_gemma":[0.9993408,0.000184978,0.00009295675,0.0001673807,0.0001481868,0.00006571058],"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":[0.001489342,0.0006762865,0.01980209,0.001012815,0.0005610972,0.0006319818,0.0001633482,0.5127375,0.03342262,0.01448291,0.1557427,0.2592773],"study_design_scores_gemma":[0.00004376475,0.00006588389,0.001088654,0.0000169614,0.00001977871,0.00006179434,0.00001085379,0.9827923,0.009347367,0.003012712,0.003523804,0.00001607958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3914162,0.007154425,0.4688427,0.002296185,0.0004645541,0.000375886,0.01905712,0.09496775,0.01542515],"genre_scores_gemma":[0.7767925,0.001488135,0.1620679,0.001108305,0.0001323512,0.0003851987,0.0418298,0.001369418,0.01482648],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01260954,"threshold_uncertainty_score":0.02507228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005098687638019777,"score_gpt":0.2017933459060781,"score_spread":0.1966946582680583,"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."}}