{"id":"W1973231190","doi":"10.1186/1471-2105-15-82","title":"Transient protein-protein interface prediction: datasets, features, algorithms, and the RAD-T predictor","year":2014,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"McGill University","keywords":"Machine learning; Computer science; Artificial intelligence; Docking (animal); Protein–protein interaction; Set (abstract data type); Predictive modelling; Interface (matter); Data mining; Algorithm; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004321701,0.0002314874,0.0001943743,0.00003250283,0.000172372,0.00008879833,0.0003177285,0.0002061791,0.000005552105],"category_scores_gemma":[0.0001402416,0.0001509603,0.0000758599,0.00007633782,0.0002942588,0.00001815523,0.0001831434,0.0001981081,0.000004863587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001152956,"about_ca_system_score_gemma":0.0000591167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001364788,"about_ca_topic_score_gemma":0.00003849032,"domain_scores_codex":[0.998841,0.00008516417,0.0003693391,0.0002177154,0.0002242341,0.0002624989],"domain_scores_gemma":[0.9990575,0.00001385815,0.0001449693,0.0006278531,0.00004932372,0.0001065194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.01411588,0.0008765619,0.00219862,0.009626485,0.001919833,0.00001445907,0.01018341,0.009477018,0.1951475,0.09369072,0.2571647,0.4055848],"study_design_scores_gemma":[0.01079609,0.001166844,0.002328358,0.0002065719,0.0002028679,0.0002875608,0.0006898937,0.3151961,0.04038103,0.002087925,0.6256281,0.001028672],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03798524,0.001931731,0.9536704,0.0005069777,0.0003059931,0.002292867,0.001365858,0.00008304238,0.001857932],"genre_scores_gemma":[0.725796,0.0002710751,0.263971,0.001552176,0.001375942,0.0004734531,0.004357729,0.00009671329,0.002105846],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6896994,"threshold_uncertainty_score":0.6155983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005182954369622684,"score_gpt":0.2152603290118356,"score_spread":0.2100773746422129,"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."}}