{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003692967,0.00023085,0.0001788495,0.00008042855,0.0003752146,0.00009102843,0.0002691604,0.0001274909,0.0001177124],"category_scores_gemma":[0.000009264012,0.0002050799,0.00008833943,0.0002080193,0.00005457768,0.00003151565,0.0002936995,0.0002854824,0.00001207949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007723392,"about_ca_system_score_gemma":0.0001306228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002638625,"about_ca_topic_score_gemma":0.00001269698,"domain_scores_codex":[0.9986172,0.00004660842,0.0004948922,0.0001822277,0.0002801523,0.0003788721],"domain_scores_gemma":[0.9990981,0.000004298562,0.0002824302,0.0004347008,0.00007183891,0.0001086472],"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":[0.001400333,0.0004379505,0.001034433,0.0003029143,0.0006206151,0.00001026951,0.001781608,0.7338839,0.00988368,0.001323234,0.1743139,0.07500713],"study_design_scores_gemma":[0.001361848,0.001315995,0.0001542798,0.00002856955,0.00004292289,0.0001491516,0.001227802,0.6045501,0.003163449,0.00006814802,0.387465,0.0004727741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3136439,0.0007750723,0.6547251,0.000426425,0.001704841,0.002303969,0.0002848848,0.0001965573,0.0259392],"genre_scores_gemma":[0.9671375,0.000122341,0.02523802,0.001361922,0.0007246481,0.0003947443,0.002129346,0.00006481803,0.002826697],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6534935,"threshold_uncertainty_score":0.8362916,"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."}}