{"id":"W1994624068","doi":"10.1101/gr.093955.109","title":"Quantitative phenotyping via deep barcode sequencing","year":2009,"lang":"en","type":"article","venue":"Genome Research","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":332,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Human Genome Research Institute; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Biology; Barcode; Deep sequencing; Computational biology; DNA sequencing; Genomics; Cancer genome sequencing; Genome; Personal genomics; Multiplex; Exome sequencing; Whole genome sequencing; Genetics; Massive parallel sequencing; Gene; Computer science; Mutation","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":[0.001627943,0.001038361,0.001017506,0.001368111,0.0005595344,0.00136018,0.00131561,0.0009021316,0.001789892],"category_scores_gemma":[0.003070119,0.0006269818,0.0007759827,0.001084324,0.0008779847,0.0007330183,0.001908748,0.001970174,0.001356875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008709627,"about_ca_system_score_gemma":0.0008701555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001776089,"about_ca_topic_score_gemma":0.003291828,"domain_scores_codex":[0.9979019,0.0003243158,0.0001221257,0.0006374504,0.0008520627,0.0001620116],"domain_scores_gemma":[0.9977694,0.0006114765,0.0003214051,0.0005974313,0.0005622504,0.0001381181],"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":[0.0001383515,0.00006100697,0.002755336,0.0001560492,0.00004896595,0.00004905289,0.00009867388,0.003336718,0.9550223,0.001959203,0.0008346064,0.03553978],"study_design_scores_gemma":[0.00003404736,0.0002117722,0.01061304,0.00003398271,0.0000676557,0.000251732,0.00007472323,0.07230163,0.8940865,0.005246527,0.0169642,0.0001141693],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1038711,0.0004065555,0.8812823,0.0002325572,0.00008656174,0.0003114897,0.007784556,0.003862695,0.002162212],"genre_scores_gemma":[0.1950047,0.0007619312,0.7903906,0.0003443215,0.00003150274,0.0009936041,0.008147066,0.0009154233,0.003410799],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001789892,"threshold_uncertainty_score":0.008609474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07971096724446586,"score_gpt":0.3630725983264532,"score_spread":0.2833616310819873,"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."}}