{"id":"W4385858785","doi":"10.1093/g3journal/jkad184","title":"Model Organism Modifier (MOM): a user-friendly Galaxy workflow to detect modifiers from genome sequencing data using <i>Caenorhabditis elegans</i>","year":2023,"lang":"en","type":"article","venue":"G3 Genes Genomes Genetics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Alberta Children's Hospital Research Institute; Compute Canada; University of Calgary","keywords":"Model organism; Organism; Biology; Identification (biology); Workflow; Genome; Caenorhabditis elegans; Computational biology; Genetic screen; Whole genome sequencing; Genetics; Computer science; Phenotype; Gene; Database","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":[0.002915444,0.00320683,0.002296142,0.002644611,0.001347339,0.003579382,0.004317077,0.002363513,0.03198456],"category_scores_gemma":[0.005183151,0.002684292,0.00342668,0.001254606,0.000780874,0.001898169,0.003678,0.00310167,0.0315607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00136202,"about_ca_system_score_gemma":0.002474953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007859421,"about_ca_topic_score_gemma":0.01192845,"domain_scores_codex":[0.9988642,0.0001390518,0.0001172372,0.0004163898,0.0003137858,0.0001493119],"domain_scores_gemma":[0.9984155,0.0005585795,0.0002196389,0.0004158802,0.0002260134,0.0001644641],"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":[0.003767525,0.0003145557,0.01835145,0.006944824,0.002281092,0.002962995,0.001946609,0.00695874,0.2068961,0.008445464,0.5800632,0.1610674],"study_design_scores_gemma":[0.001244947,0.0005003183,0.02277342,0.0006792081,0.0006016739,0.002599267,0.0003868344,0.04837422,0.1912854,0.01361615,0.7169379,0.0010006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"software","genre_scores_codex":[0.008538597,0.001036459,0.2104812,0.0004963447,0.000253071,0.0006295201,0.08023529,0.6936934,0.004636234],"genre_scores_gemma":[0.04313598,0.001316991,0.5750284,0.001734111,0.0001029571,0.003189457,0.2534868,0.1103038,0.01170152],"genre_candidate":"software","genre_consensus":"software","teacher_disagreement_score":0.03198456,"threshold_uncertainty_score":0.106999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05377255734827517,"score_gpt":0.270434008638506,"score_spread":0.2166614512902308,"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."}}