{"id":"W2463458349","doi":"10.1016/j.ymeth.2016.06.020","title":"Conservation of hot regions in protein–protein interaction in evolution","year":2016,"lang":"en","type":"article","venue":"Methods","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Hot spot (computer programming); Computer science; Sequence (biology); Protein sequencing; Similarity (geometry); Algorithm; Artificial intelligence; Data mining; Peptide sequence; Biology; Genetics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001657947,0.0003533279,0.0005444903,0.001465552,0.0007058232,0.0006315112,0.0007765238,0.0005747371,0.002588937],"category_scores_gemma":[0.00182209,0.0003661291,0.0005931618,0.001008391,0.0007193451,0.0005410804,0.0007300142,0.0007614245,0.0004863367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005407438,"about_ca_system_score_gemma":0.0003145777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006440164,"about_ca_topic_score_gemma":0.001764043,"domain_scores_codex":[0.999117,0.0002961259,0.00006687467,0.0002010802,0.0002118574,0.0001071454],"domain_scores_gemma":[0.9984024,0.0007457504,0.0003457417,0.0002258251,0.0001164102,0.0001638085],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002667715,0.0003544438,0.08994588,0.0009216132,0.0008792859,0.001613421,0.00125928,0.01202632,0.7712889,0.01685407,0.001255174,0.100934],"study_design_scores_gemma":[0.0003103094,0.0009272773,0.5477883,0.0002635625,0.001028066,0.008774823,0.0008863952,0.07301161,0.3294358,0.01721212,0.02014077,0.0002208547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.973286,0.001718407,0.0227169,0.0001065352,0.00003681726,0.00003607984,0.0002948108,0.0001629596,0.001641491],"genre_scores_gemma":[0.9914168,0.0002535203,0.007237232,0.00007188214,0.00001821394,0.00003036989,0.0002453632,0.00005710311,0.0006696662],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002588937,"threshold_uncertainty_score":0.008768201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02040540416355199,"score_gpt":0.3408698177544558,"score_spread":0.3204644135909038,"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."}}