{"id":"W1970025484","doi":"10.1186/1471-2105-11-s10-p1","title":"Human protein-protein interaction prediction","year":2010,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Biotechnology and Biological Sciences Research Council; Canadian Institutes of Health Research","keywords":"Computer science; UniProt; Interactome; RefSeq; Gene ontology; Data mining; Set (abstract data type); Feature (linguistics); Similarity (geometry); Domain (mathematical analysis); Bayesian probability; Information retrieval; Machine learning; Artificial intelligence; Biology; Gene; Mathematics; Genome","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002783879,0.0001924448,0.0001281946,0.00006645357,0.0001895419,0.00009325027,0.0002313698,0.0003025049,0.00006565079],"category_scores_gemma":[0.00004441059,0.000177474,0.0001027479,0.00008277504,0.00007954573,0.00002726023,0.0001284896,0.0003266187,0.000105979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001335391,"about_ca_system_score_gemma":0.00006542687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008068862,"about_ca_topic_score_gemma":0.00009927475,"domain_scores_codex":[0.9989017,0.00001504672,0.0005054864,0.0001430139,0.0001570908,0.0002776525],"domain_scores_gemma":[0.9990602,0.000003239144,0.000235217,0.0005010232,0.00008714425,0.0001132483],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007411572,0.0001202519,0.0007771213,0.0002810929,0.00007113379,6.173076e-7,0.0003000363,0.000421302,0.9691684,0.005614185,0.009554315,0.01361744],"study_design_scores_gemma":[0.00397095,0.001720771,0.004257646,0.0002303255,0.00009135002,0.0001946731,0.001505172,0.2923204,0.330175,0.003518736,0.3601378,0.001877185],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8962552,0.00002288009,0.08122174,0.00005316614,0.0006743193,0.0008540462,0.00004962229,0.00007914228,0.02078992],"genre_scores_gemma":[0.9190194,0.000003693554,0.07751475,0.0001756418,0.0007131784,0.00007995726,0.0004204651,0.00003000393,0.00204292],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6389934,"threshold_uncertainty_score":0.723718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01079829003295911,"score_gpt":0.2420439317933605,"score_spread":0.2312456417604014,"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."}}