{"id":"W1982057578","doi":"10.1089/cmb.2004.11.1001","title":"Pooled Genomic Indexing (PGI): Analysis and Design of Experiments","year":2004,"lang":"en","type":"article","venue":"Journal of Computational Biology","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Université de Montréal; National Human Genome Research Institute; Howard Hughes Medical Institute","keywords":"Shotgun sequencing; Pooling; Shotgun; Sequence (biology); clone (Java method); Search engine indexing; Intersection (aeronautics); Genetics; Computational biology; Biology; Sequence-tagged site; Row; Contig; Alignment-free sequence analysis; Probabilistic logic; Sequence analysis; Column (typography); Computer science; Chromosome; Sequence alignment; DNA sequencing; Genome; Artificial intelligence; Gene; Peptide sequence; Gene mapping; Engineering; Database","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":[0.0001589349,0.00005777165,0.0001458949,0.000163371,0.00002472465,0.000005069223,0.00008121879,0.00006566204,0.000008378267],"category_scores_gemma":[0.00002500352,0.00004884779,0.00006879072,0.0001120741,0.00005386208,0.000003086714,0.00002567709,0.00004076003,4.309135e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001442067,"about_ca_system_score_gemma":0.0001177628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002598894,"about_ca_topic_score_gemma":4.272441e-7,"domain_scores_codex":[0.9994342,0.00006328902,0.0002785193,0.00009650904,0.00006556166,0.00006187643],"domain_scores_gemma":[0.9994272,0.00001747326,0.0003105231,0.00005684547,0.0001481033,0.00003987852],"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":[0.0001186388,0.00003805888,0.003909488,0.000002237032,0.0003318605,5.510076e-7,0.00005547435,0.10487,0.8891742,0.0002951222,0.00003568261,0.001168757],"study_design_scores_gemma":[0.004317766,0.001708968,0.1993416,0.00003100364,0.0004134245,0.000109512,0.0002576303,0.001572593,0.7704492,0.01991852,0.001583275,0.000296568],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6068239,0.0006590735,0.3923277,0.00009547108,0.00004480657,0.00002741942,0.000001929486,6.856851e-7,0.00001903937],"genre_scores_gemma":[0.9800603,0.00008421453,0.01965058,0.000109566,0.00006267335,0.000001329396,0.00001841336,0.000003534499,0.000009394572],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3732364,"threshold_uncertainty_score":0.1991955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01955449999941091,"score_gpt":0.3009634660872478,"score_spread":0.2814089660878369,"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."}}