{"id":"W4283716699","doi":"10.1093/bioinformatics/btac429","title":"RAPPPID: towards generalizable protein interaction prediction with AWD-LSTM twin networks","year":2022,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"Compute Canada; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Computer science; Regularization (linguistics); Machine learning; Artificial intelligence; Source code; Code (set theory); Training set; Set (abstract data type); Data mining","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":[0.001226411,0.001598749,0.001027967,0.0007147337,0.0004110689,0.001079532,0.003183093,0.00176477,0.003471207],"category_scores_gemma":[0.003530415,0.0009008757,0.001028169,0.000809017,0.0006598642,0.002173152,0.002769174,0.003164164,0.002002057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001021728,"about_ca_system_score_gemma":0.001183798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005374288,"about_ca_topic_score_gemma":0.008227117,"domain_scores_codex":[0.9995989,0.00007944469,0.00001685228,0.0001747561,0.00008568975,0.00004445916],"domain_scores_gemma":[0.9993584,0.0002828374,0.00006006076,0.0001245322,0.0001058945,0.00006838403],"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":[0.0005569049,0.0002517359,0.002809357,0.0004004104,0.0003361905,0.0003076513,0.0001534998,0.6965268,0.01504534,0.008558291,0.02340573,0.2516481],"study_design_scores_gemma":[0.00001187815,0.00001374564,0.00008829739,0.000004681724,0.000006819317,0.00001678382,0.000005014582,0.9949231,0.001113811,0.003322934,0.0004882402,0.000004809066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04841448,0.0009500531,0.926984,0.0006325506,0.0001689826,0.0001285445,0.002541923,0.01792094,0.002258512],"genre_scores_gemma":[0.4384512,0.0006283835,0.5393893,0.0009198909,0.0001212565,0.0005043685,0.01057354,0.001202591,0.008209421],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005374288,"threshold_uncertainty_score":0.0116123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008924453457807548,"score_gpt":0.2101870033833415,"score_spread":0.2012625499255339,"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."}}