{"id":"W4291906074","doi":"10.1101/2022.08.11.503701","title":"Aldy 4: An efficient genotyper and star-allele caller for pharmacogenomics","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Genotyping; Genetics; Computational biology; Pharmacogenomics; 1000 Genomes Project; DNA sequencing; Genomics; Copy-number variation; Biology; Personal genomics; Allele; Haplotype; Genome; Massive parallel sequencing; Illumina dye sequencing; Computer science; Genotype; Single-nucleotide polymorphism; Gene","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":[0.005436982,0.001531855,0.001726204,0.001456067,0.0006518,0.001701209,0.002316318,0.001777599,0.02164627],"category_scores_gemma":[0.008522626,0.0015372,0.0012947,0.001050573,0.0007030657,0.001448226,0.002432473,0.002378119,0.01558828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007841479,"about_ca_system_score_gemma":0.002123725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001674292,"about_ca_topic_score_gemma":0.002290731,"domain_scores_codex":[0.9966067,0.000802384,0.0002456221,0.0008265709,0.001339334,0.0001795249],"domain_scores_gemma":[0.9963734,0.00171149,0.0004196464,0.0007804496,0.0004983596,0.0002166724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004481893,0.0003623547,0.01250007,0.001510523,0.001125123,0.0007909258,0.0005117526,0.03542522,0.2582894,0.01212626,0.2085194,0.4643571],"study_design_scores_gemma":[0.001318584,0.0007133571,0.01088164,0.0001579679,0.0002844271,0.001452233,0.00008098174,0.4203038,0.3230404,0.02160281,0.2195838,0.0005800855],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.01464621,0.0005107839,0.781915,0.000420838,0.0002046646,0.000306858,0.01185531,0.1874876,0.002652749],"genre_scores_gemma":[0.05427697,0.0002971152,0.8904258,0.0007276003,0.000108506,0.0009540344,0.02032319,0.02396297,0.008923839],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.02164627,"threshold_uncertainty_score":0.07241398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01534716180575069,"score_gpt":0.2388007537554802,"score_spread":0.2234535919497295,"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."}}