{"id":"W2159640103","doi":"10.1093/bioinformatics/btq216","title":"Next-generation VariationHunter: combinatorial algorithms for transposon insertion discovery","year":2010,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genome Rearrangement Algorithms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":221,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Genome British Columbia; National Human Genome Research Institute; Howard Hughes Medical Institute","keywords":"Transposable element; Algorithm; Genome; Computer science; Human genome; Structural variation; Scope (computer science); Computational biology; Biology; Genetics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000225154,0.0001485966,0.000112701,0.00005356645,0.0001223249,0.0001449385,0.0001550304,0.0001969554,0.00001004317],"category_scores_gemma":[0.00005894599,0.0001437626,0.00009757781,0.00006896388,0.00003207286,0.00005474866,0.00002692516,0.00008631478,0.0000135245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001439396,"about_ca_system_score_gemma":0.00006668355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005654709,"about_ca_topic_score_gemma":0.00002371173,"domain_scores_codex":[0.9991543,0.00001245638,0.0003188709,0.0001527876,0.0001547339,0.0002068474],"domain_scores_gemma":[0.999423,0.000009249713,0.0001196726,0.0002692263,0.0001194893,0.00005932584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005823228,0.00009757836,0.0002227848,0.00006135858,0.00005635305,1.560039e-7,0.0002542739,0.00004919974,0.9820741,0.002122373,0.001522455,0.01348109],"study_design_scores_gemma":[0.007180573,0.001607073,0.002668848,0.00002164735,0.0001683118,0.00002257148,0.0003274245,0.205182,0.5917528,0.001125607,0.1887559,0.001187266],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6862942,0.00004404923,0.3081792,0.0001984591,0.00369103,0.000875853,0.0001296174,0.00003746923,0.0005500991],"genre_scores_gemma":[0.9315189,0.00005906321,0.06319182,0.0001887669,0.001971422,0.0001180525,0.002586899,0.00003286485,0.0003322207],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3903214,"threshold_uncertainty_score":0.586247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02485141532122126,"score_gpt":0.2506231169510057,"score_spread":0.2257717016297844,"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."}}