{"id":"W4281940572","doi":"10.3389/fbinf.2022.896295","title":"ContactPFP: Protein Function Prediction Using Predicted Contact Information","year":2022,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics; National Science Foundation; National Institutes of Health; Division of Civil, Mechanical and Manufacturing Innovation; National Institute of General Medical Sciences; Ministry of Education, Culture, Sports, Science and Technology","keywords":"Protein function prediction; Computer science; Protein structure prediction; Computational biology; Protein function; Structural genomics; Function (biology); Data mining; Sequence (biology); Protein structure database; Protein structure; Bioinformatics; Artificial intelligence; Biology; Gene; Genetics; Sequence database","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009370632,0.001600911,0.001247346,0.002299307,0.0008299007,0.0008095826,0.00147993,0.001520209,0.004474667],"category_scores_gemma":[0.004296323,0.0004346282,0.00117322,0.001790023,0.0003497777,0.001669244,0.001274949,0.0009996364,0.002052733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000629195,"about_ca_system_score_gemma":0.0009216852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002292854,"about_ca_topic_score_gemma":0.002379309,"domain_scores_codex":[0.9992216,0.00008075536,0.00005630003,0.0001928178,0.0003728107,0.0000756301],"domain_scores_gemma":[0.9989125,0.0005204117,0.0001338108,0.0001667215,0.0001987613,0.00006784902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003518954,0.001051772,0.05676638,0.004041816,0.0009044328,0.003102008,0.0005526193,0.1463359,0.1354132,0.0145982,0.128658,0.5050568],"study_design_scores_gemma":[0.000157553,0.0003128808,0.0113746,0.00006481219,0.0001051921,0.001430816,0.00008107423,0.9012128,0.06104446,0.005301777,0.01881306,0.0001010023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2666442,0.003938362,0.5752611,0.0004762202,0.000285097,0.0006353973,0.0224871,0.1216018,0.008670758],"genre_scores_gemma":[0.6345051,0.001644076,0.310732,0.000237032,0.0001076051,0.0006891984,0.0468018,0.002137304,0.003145809],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004474667,"threshold_uncertainty_score":0.01496929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004371013926252199,"score_gpt":0.1913359052639602,"score_spread":0.186964891337708,"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."}}