{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05169096,0.003492679,0.004353539,0.001927821,0.001072547,0.002927272,0.002908462,0.002365733,0.00415489],"category_scores_gemma":[0.09528127,0.001852679,0.00364138,0.001928852,0.002064285,0.00167791,0.002213715,0.002883592,0.0008787061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003155129,"about_ca_system_score_gemma":0.005301754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00105807,"about_ca_topic_score_gemma":0.000752417,"domain_scores_codex":[0.946884,0.03889372,0.0024846,0.006149539,0.004060104,0.001527973],"domain_scores_gemma":[0.9280959,0.05362044,0.005058424,0.008402482,0.004024974,0.0007976508],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.03608491,0.002895599,0.01410912,0.007210946,0.007889654,0.0004801511,0.00122223,0.3443331,0.04370258,0.06847781,0.006483795,0.4671101],"study_design_scores_gemma":[0.006611662,0.01802851,0.007404095,0.0002911393,0.003032509,0.0001703357,0.000240122,0.8223373,0.048213,0.07600863,0.01731306,0.0003495771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02238177,0.0002378119,0.9640874,0.000120603,0.0001515481,0.0098632,0.0008085162,0.001455512,0.0008936358],"genre_scores_gemma":[0.08499375,0.0002167379,0.8505626,0.0002444314,0.0000744049,0.06209747,0.0009108136,0.0002699195,0.0006298359],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05169096,"threshold_uncertainty_score":0.2733712,"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."}}