{"id":"W2163680443","doi":"10.1126/scisignal.260pe11","title":"How Perfect Can Protein Interactomes Be?","year":2009,"lang":"en","type":"article","venue":"Science Signaling","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Computational biology; Biology; Proteome; Protein–protein interaction; Organism; Spurious relationship; Genome; Fraction (chemistry); Model organism; Computer science; Genetics; Gene; Machine learning; Chemistry","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":[0.0003726486,0.00009653658,0.0000752468,0.00005223969,0.0001834302,0.0002007076,0.0003105176,0.00004999227,0.000007515398],"category_scores_gemma":[0.00005542531,0.00008121954,0.00004604577,0.000175597,0.0001469132,0.00001181415,0.00005911165,0.00008235945,0.000001943298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001655152,"about_ca_system_score_gemma":0.0001224105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003548275,"about_ca_topic_score_gemma":0.000004631778,"domain_scores_codex":[0.9992036,0.00001053041,0.0001042324,0.0002208406,0.0001616293,0.0002991583],"domain_scores_gemma":[0.9995692,0.000002887451,0.00005811367,0.0002111077,0.00006614167,0.00009252445],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000006139551,0.000007782577,0.00003421812,0.000002616787,0.000002642532,7.256252e-7,0.0001134706,0.00007465247,0.9859868,0.0002754421,0.0001356455,0.01335981],"study_design_scores_gemma":[0.0001684644,0.0003065582,0.0001716889,0.0000268604,0.000003933493,0.00001131864,0.0002863373,0.001374324,0.9900377,0.0007545208,0.006646561,0.0002117084],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9923375,0.0001568789,0.00365113,0.001791173,0.0001100762,0.000157423,0.000003336373,0.0000124085,0.001780029],"genre_scores_gemma":[0.9965725,0.000005097357,0.001966037,0.0007177106,0.0001647231,0.000003635833,0.000008862778,0.000004315627,0.0005571189],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01314811,"threshold_uncertainty_score":0.3312037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009351868958674295,"score_gpt":0.2375925917934143,"score_spread":0.22824072283474,"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."}}