{"id":"W3189581986","doi":"10.1186/s12864-021-07892-9","title":"InvertypeR: Bayesian inversion genotyping with Strand-seq data","year":2021,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Genomic variations and chromosomal abnormalities","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Cancer Agency","funders":"Canadian Institutes of Health Research","keywords":"Genotyping; Biology; Chromosomal inversion; Computational biology; Genetics; Bayesian probability; Genome; Structural variation; Genomics; Inversion (geology); Molecular Inversion Probe; Reference genome; 1000 Genomes Project; Algorithm; Computer science; Genotype; Chromosome; Artificial intelligence; Gene; Single-nucleotide polymorphism","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":[0.007462183,0.001452183,0.001247871,0.002114547,0.00084408,0.001901327,0.002034408,0.001338664,0.008670274],"category_scores_gemma":[0.01223189,0.001544981,0.001701408,0.001212034,0.000714462,0.0008786048,0.002270048,0.00196198,0.004569832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006321269,"about_ca_system_score_gemma":0.001904096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002147649,"about_ca_topic_score_gemma":0.007106828,"domain_scores_codex":[0.9958759,0.001081374,0.0002793881,0.001558222,0.001023972,0.0001812788],"domain_scores_gemma":[0.9920859,0.00442925,0.001265674,0.001356439,0.0005455228,0.0003171285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003512118,0.0006100844,0.1087141,0.004151872,0.004581847,0.001541001,0.001721242,0.05495772,0.3929775,0.02172309,0.102425,0.3030844],"study_design_scores_gemma":[0.001552427,0.001001749,0.06588349,0.0005291512,0.001342423,0.002776146,0.0003301765,0.4478512,0.2722189,0.03899118,0.1665479,0.0009753449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05907882,0.0007028589,0.810118,0.0004014845,0.0003806174,0.0007522425,0.06148106,0.06147672,0.005608138],"genre_scores_gemma":[0.1546272,0.0004116409,0.7741343,0.0007243098,0.0001662419,0.001483144,0.05392722,0.01148491,0.003040927],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008670274,"threshold_uncertainty_score":0.03946429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02437393269784905,"score_gpt":0.2260911998465747,"score_spread":0.2017172671487257,"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."}}