{"id":"W4390793818","doi":"10.1093/bioinformatics/btad738","title":"Seq-InSite: sequence supersedes structure for protein interaction site prediction","year":2024,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Computer science; Sequence (biology); Source code; Matching (statistics); Code (set theory); Quality (philosophy); Data mining; Artificial intelligence; Machine learning; Programming language; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002792556,0.002313701,0.001623517,0.001620017,0.00127392,0.001690918,0.003917534,0.001402145,0.01319833],"category_scores_gemma":[0.007951347,0.001155572,0.001975104,0.001898216,0.0008707204,0.002112783,0.002297148,0.003512391,0.01150674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009107405,"about_ca_system_score_gemma":0.003025045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005582646,"about_ca_topic_score_gemma":0.01183582,"domain_scores_codex":[0.9987747,0.0003427544,0.00007527866,0.0003371681,0.0003814746,0.00008867228],"domain_scores_gemma":[0.9978473,0.001185569,0.0002271717,0.0002561539,0.0002710244,0.0002128385],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00409116,0.001092479,0.0271884,0.003775148,0.001213432,0.0008523109,0.0006215721,0.1400435,0.04627523,0.03013718,0.6075265,0.1371831],"study_design_scores_gemma":[0.0005176351,0.0004342123,0.003729367,0.0002030267,0.0002040319,0.00053467,0.0001459337,0.8526767,0.03152904,0.02892398,0.0809468,0.0001545534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08294156,0.001625814,0.4894089,0.001921793,0.0008690408,0.0005664194,0.09086812,0.3173262,0.01447219],"genre_scores_gemma":[0.1908158,0.001081745,0.5031477,0.001108881,0.0002205745,0.001240015,0.2695374,0.02580376,0.00704413],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01319833,"threshold_uncertainty_score":0.04415274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01073657310424628,"score_gpt":0.2563578867487379,"score_spread":0.2456213136444917,"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."}}