{"id":"W2147087392","doi":"10.1093/nar/gkr1294","title":"MUSI: an integrated system for identifying multiple specificity from very large peptide or nucleic acid data sets","year":2011,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Biology; Computational biology; DNA microarray; Nucleic acid; Pipeline (software); Transcription (linguistics); Genetics; Bioinformatics; Computer science; Gene; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001488044,0.0002752848,0.0003062963,0.0001280476,0.000462495,0.0001820236,0.001871348,0.0003538705,0.0002260032],"category_scores_gemma":[0.0004190906,0.000243661,0.0000999777,0.0002330521,0.0001583009,0.00003689561,0.001356224,0.000378943,0.0001066187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001174237,"about_ca_system_score_gemma":0.0002346159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005675094,"about_ca_topic_score_gemma":0.001786111,"domain_scores_codex":[0.9969805,0.0002711446,0.0004356754,0.00106309,0.0004158463,0.0008337809],"domain_scores_gemma":[0.9970392,0.00006878188,0.0001131923,0.002109885,0.0004041575,0.0002648074],"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":[0.001494865,0.0005265329,0.006099944,0.0002361977,0.0002449952,0.00005478633,0.0009847996,0.000006155209,0.9769326,0.0001988822,0.00831829,0.004901891],"study_design_scores_gemma":[0.01129073,0.003559601,0.04969083,0.0006131969,0.0002068143,0.0001169365,0.0447927,0.2445419,0.4828244,0.0008647047,0.1586667,0.002831495],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9881409,0.0001703759,0.006964235,0.00001793562,0.0003133445,0.0006728743,0.002853721,0.00005800766,0.0008085635],"genre_scores_gemma":[0.9758635,0.00009070063,0.01854554,0.00003793076,0.0003449666,0.00005427243,0.004597815,0.0001067932,0.000358445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4941083,"threshold_uncertainty_score":0.9936206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1474142261369567,"score_gpt":0.3545104690136365,"score_spread":0.2070962428766798,"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."}}