{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008465846,0.001229225,0.00119188,0.00268037,0.0007876412,0.0009124216,0.001006147,0.001193279,0.03898544],"category_scores_gemma":[0.001981307,0.0003494341,0.001310128,0.003627773,0.0002460045,0.0006574923,0.001046039,0.0008071532,0.03809793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004331842,"about_ca_system_score_gemma":0.001030194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002111914,"about_ca_topic_score_gemma":0.002346206,"domain_scores_codex":[0.9992466,0.0001327711,0.00005408625,0.0002286105,0.0002378829,0.0001001253],"domain_scores_gemma":[0.9995572,0.0001717681,0.00003770618,0.00007537184,0.0001123336,0.0000455876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001449501,0.0003016251,0.03331571,0.003933835,0.0003466853,0.002078158,0.0001363004,0.008927558,0.01806504,0.004300923,0.7244041,0.2027407],"study_design_scores_gemma":[0.0004561023,0.000692531,0.07812665,0.0009929533,0.0006042088,0.008725137,0.0003457971,0.09775957,0.02736512,0.02096855,0.7637873,0.0001761183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.07831988,0.02056055,0.1051859,0.001771866,0.0008661762,0.0008403879,0.7149466,0.02343835,0.05407042],"genre_scores_gemma":[0.1886203,0.00379036,0.09326645,0.001063865,0.0002648315,0.0006456446,0.701956,0.0007937217,0.009598842],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03898544,"threshold_uncertainty_score":0,"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."}}