{"id":"W2111805188","doi":"10.1186/1752-0509-8-35","title":"Improving protein function prediction using domain and protein complexes in PPI networks","year":2014,"lang":"en","type":"article","venue":"BMC Systems Biology","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"National Natural Science Foundation of China","keywords":"Protein function prediction; Domain (mathematical analysis); Computational biology; Computer science; In silico; Context (archaeology); Protein domain; Similarity (geometry); Protein–protein interaction; Protein function; Structural similarity; Function (biology); Protein Interaction Networks; Systems biology; Data mining; Machine learning; Biology; Artificial intelligence; Genetics; Mathematics; Gene","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006761664,0.0001675224,0.0002184443,0.0000631678,0.0000937608,0.00003625077,0.00009030355,0.0003627313,0.000001671624],"category_scores_gemma":[0.00002991529,0.0001508595,0.00003704467,0.00007188733,0.00008434351,0.000006465764,0.0001003814,0.0001185247,0.000001576427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002480838,"about_ca_system_score_gemma":0.00003321637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002480609,"about_ca_topic_score_gemma":0.0001825983,"domain_scores_codex":[0.9987134,0.0002280813,0.0004003392,0.0003111674,0.00004389943,0.0003031406],"domain_scores_gemma":[0.999464,0.00001161804,0.0001938694,0.0002355977,0.00003629471,0.00005863768],"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":[0.0002170699,0.00003076916,0.0182702,0.0002287171,0.00003585438,3.394199e-7,0.00003936705,0.004943568,0.9652941,0.006622722,0.00007652402,0.00424081],"study_design_scores_gemma":[0.003526472,0.001859395,0.007861475,0.0003447386,0.00003620547,0.0001102963,0.0004508284,0.9604403,0.002763534,0.003400855,0.01833778,0.000868139],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5890409,0.000652859,0.4093161,0.000005675949,0.000222819,0.0005945018,0.000006888577,0.00001282403,0.0001474247],"genre_scores_gemma":[0.9967625,0.00000490703,0.002230825,0.00003248439,0.0006573088,0.0000983885,0.0001122852,0.00001744235,0.00008392721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9625306,"threshold_uncertainty_score":0.6151873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01036805990849688,"score_gpt":0.209650216361589,"score_spread":0.1992821564530921,"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."}}