{"id":"W2892884209","doi":"10.1038/s41598-018-32834-z","title":"Pattern to Knowledge: Deep Knowledge-Directed Machine Learning for Residue-Residue Interaction Prediction","year":2018,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Discriminative model; Computer science; Residue (chemistry); Deep learning; Artificial intelligence; Construct (python library); Machine learning; Computational biology; Bioinformatics; Biology; Biochemistry","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":[0.001438784,0.001366943,0.001315137,0.001430518,0.0004677002,0.001128856,0.00237785,0.001823882,0.00250777],"category_scores_gemma":[0.005493707,0.0005985333,0.001073028,0.001632527,0.0006930319,0.001751856,0.001852975,0.003072268,0.001120389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009874983,"about_ca_system_score_gemma":0.001877513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005203488,"about_ca_topic_score_gemma":0.007874225,"domain_scores_codex":[0.9993254,0.0001740524,0.00004519479,0.0002380362,0.0001441836,0.0000732246],"domain_scores_gemma":[0.9978866,0.001304154,0.0001786998,0.0003129569,0.0002141884,0.000103348],"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":[0.0005717297,0.0006168983,0.008963286,0.0005998705,0.0004139203,0.0002881083,0.0001296322,0.4207794,0.006485831,0.01192245,0.02526969,0.5239592],"study_design_scores_gemma":[0.00002110032,0.00004870681,0.0002874265,0.00001694576,0.00002410086,0.00002654255,0.00001087586,0.9834067,0.001757713,0.01326949,0.001119757,0.00001057559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08191806,0.002243148,0.8955785,0.001174,0.0002295098,0.0001983735,0.004136757,0.01085294,0.003668638],"genre_scores_gemma":[0.6058558,0.0009834399,0.377212,0.001081656,0.0001631509,0.0004864946,0.01041299,0.0004435848,0.003360948],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005203488,"threshold_uncertainty_score":0.01034641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0267575839912762,"score_gpt":0.3304733810031875,"score_spread":0.3037157970119113,"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."}}