{"id":"W2140743149","doi":"10.1093/bib/bbp058","title":"Genome variation discovery with high-throughput sequencing data","year":2010,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research","keywords":"Genome; DNA sequencing; Computer science; Computational biology; Structural variation; Identification (biology); Genomics; Throughput; Human genome; Biology; Genetics; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005521046,0.0009358538,0.001661268,0.004004764,0.0006198331,0.00254283,0.001373191,0.001258306,0.001154457],"category_scores_gemma":[0.01693938,0.0007557717,0.001462271,0.006044765,0.0005648381,0.001433343,0.001233034,0.001909654,0.001114854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005824141,"about_ca_system_score_gemma":0.0009235364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001640981,"about_ca_topic_score_gemma":0.002470767,"domain_scores_codex":[0.9957956,0.001879369,0.0001932549,0.0008014336,0.001187264,0.0001430323],"domain_scores_gemma":[0.9901189,0.007228129,0.0004857381,0.00125239,0.0007161406,0.0001987598],"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":[0.0007231057,0.0001588715,0.03758801,0.001960898,0.001754904,0.002096293,0.0004976076,0.0966678,0.06708483,0.03622869,0.03107733,0.7241616],"study_design_scores_gemma":[0.0002099197,0.0002210685,0.03988369,0.0003845243,0.000807675,0.00306601,0.0002897992,0.5549556,0.06226354,0.2233678,0.1142003,0.0003501278],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03309569,0.005643154,0.9452046,0.001538243,0.0002709944,0.0001605787,0.008148678,0.004428454,0.001509519],"genre_scores_gemma":[0.1363852,0.006517402,0.8375944,0.0007441457,0.0004526088,0.000271503,0.01626443,0.0005380853,0.001232159],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005521046,"threshold_uncertainty_score":0.02919841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01429686147796803,"score_gpt":0.2234970998298424,"score_spread":0.2092002383518744,"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."}}