{"id":"W4318913152","doi":"10.1073/pnas.2202584120","title":"Transcriptomic congruence analysis for evaluating model organisms","year":2023,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Addiction and Mental Health","funders":"U.S. National Library of Medicine; National Institute of General Medical Sciences; National Cancer Institute; National Institutes of Health","keywords":"Congruence (geometry); Computational biology; Concordance; Model organism; Organism; Biology; Drug development; Computer science; Data science; Gene; Bioinformatics; Psychology; Drug; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00712094,0.0009975134,0.0007680361,0.003098194,0.0008047509,0.002021876,0.001077404,0.0006057283,0.001703206],"category_scores_gemma":[0.02414066,0.0003131724,0.001551049,0.001991263,0.001562825,0.001392765,0.002496988,0.001078467,0.0002450602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001118015,"about_ca_system_score_gemma":0.0009880404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007873077,"about_ca_topic_score_gemma":0.000792674,"domain_scores_codex":[0.9961233,0.001920887,0.0003431537,0.0007343147,0.0007440597,0.000134304],"domain_scores_gemma":[0.9893218,0.006957678,0.001338172,0.00120433,0.0007859237,0.0003920826],"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":[0.001316948,0.0002983891,0.1303017,0.001092221,0.0008545262,0.0008806704,0.001700929,0.3034025,0.1748928,0.1979481,0.00216932,0.1851419],"study_design_scores_gemma":[0.00004654521,0.0004927987,0.02512585,0.00006298623,0.0002202011,0.0002974177,0.0006615979,0.7350402,0.02480536,0.2058592,0.007313509,0.00007429984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1114964,0.0001723096,0.8844997,0.0001313009,0.00003052056,0.0001819294,0.001149195,0.0005576609,0.001780931],"genre_scores_gemma":[0.555205,0.0001445617,0.4407428,0.00008868307,0.00003735647,0.0005362756,0.002798024,0.0001906095,0.0002568254],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00712094,"threshold_uncertainty_score":0.03765959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05876788672679242,"score_gpt":0.3371309343598293,"score_spread":0.2783630476330369,"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."}}