{"id":"W2158564991","doi":"10.1186/1471-2105-8-239","title":"Probabilistic prediction and ranking of human protein-protein interactions","year":2007,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":134,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research","keywords":"Ranking (information retrieval); Computer science; Probabilistic logic; Bayes' theorem; Naive Bayes classifier; False positive rate; Set (abstract data type); False discovery rate; Bayesian probability; Data mining; Machine learning; Protein–protein interaction; Protein function prediction; Human proteome project; Bayesian network; Interaction network; Function (biology); Computational biology; Artificial intelligence; Protein function; Proteomics; Biology; Support vector machine; Genetics","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":[0.001743711,0.0008898777,0.0007367349,0.002214934,0.0003760927,0.0007645804,0.0006331474,0.0008088317,0.002111154],"category_scores_gemma":[0.005357726,0.0002970879,0.0006741079,0.001698099,0.0002983015,0.0005227642,0.0007592504,0.0005870907,0.001205178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005493336,"about_ca_system_score_gemma":0.0005957848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001998206,"about_ca_topic_score_gemma":0.003441972,"domain_scores_codex":[0.9980918,0.0005684923,0.0001065064,0.0004530927,0.0006628214,0.000117356],"domain_scores_gemma":[0.9969896,0.001902388,0.0004202485,0.0002050465,0.0003906036,0.00009202653],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00187648,0.0004655133,0.1688903,0.001675066,0.001025316,0.0007136377,0.0001665253,0.4067006,0.05070755,0.004742488,0.02273534,0.3403012],"study_design_scores_gemma":[0.00006058102,0.0002200273,0.04806539,0.00005520206,0.0001362704,0.0008984605,0.00004036367,0.922437,0.01570847,0.008025716,0.00429973,0.00005279619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4999891,0.005513181,0.468848,0.0006011541,0.00009690404,0.000216433,0.01464548,0.004345753,0.005743999],"genre_scores_gemma":[0.8729041,0.0008507106,0.1131186,0.0001047489,0.00007539405,0.000119822,0.01170695,0.0001110131,0.00100865],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002214934,"threshold_uncertainty_score":0.009221673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01380876975229888,"score_gpt":0.2508298393515291,"score_spread":0.2370210695992302,"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."}}