{"id":"W2139830657","doi":"10.1093/nar/gkp820","title":"DRYGIN: a database of quantitative genetic interaction networks in yeast","year":2009,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":95,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Ontario Genomics; Ontario Genomics Institute; Genome Canada","keywords":"Biology; Computational biology; Genome; Annotation; Gene interaction; Ontology; Gene ontology; Interface (matter); Genetic network; Genetic screen; Gene; Interaction network; Genetics; Phenotype; Gene expression","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001270045,0.002487322,0.00212535,0.006885672,0.0007785317,0.002712287,0.003206856,0.001170275,0.01099902],"category_scores_gemma":[0.004052625,0.001327072,0.001612353,0.00956846,0.0003742113,0.001955126,0.00200262,0.001533827,0.00705719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001035655,"about_ca_system_score_gemma":0.002089146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004754573,"about_ca_topic_score_gemma":0.006252635,"domain_scores_codex":[0.9992822,0.0001226569,0.0001381019,0.0001881739,0.0002185611,0.00005030876],"domain_scores_gemma":[0.9985439,0.0006296576,0.0002357017,0.000305712,0.0001557835,0.0001292274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003209234,0.0004107956,0.02176075,0.01590353,0.002227477,0.00290088,0.001321784,0.09719794,0.06282847,0.04505475,0.5736316,0.1735528],"study_design_scores_gemma":[0.001203452,0.0002036926,0.02425364,0.0007358515,0.0007433307,0.002184931,0.0003807646,0.07961849,0.02222974,0.06183445,0.8061638,0.0004478518],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01104553,0.002415679,0.05500552,0.0001874369,0.00006340271,0.0001534854,0.8734568,0.05363775,0.004034248],"genre_scores_gemma":[0.0259668,0.002260607,0.04383038,0.00008036415,0.00001595603,0.0005869108,0.923928,0.002505377,0.0008256425],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01099902,"threshold_uncertainty_score":0.03679538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.038317279845149,"score_gpt":0.3525779688748422,"score_spread":0.3142606890296932,"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."}}