{"id":"W4404691004","doi":"10.1093/bioinformatics/btae707","title":"<i>lefser</i>: implementation of metagenomic biomarker discovery tool, <i>LEfSe</i>, in R","year":2024,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Population and Public Health","funders":"National Cancer Institute; National Institutes of Health","keywords":"Bioconductor; Python (programming language); Metagenomics; Computer science; Software; Data mining; Source code; JavaScript; Benchmarking; Visualization; Population; Data science; Biology; World Wide Web; Operating system","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001901888,0.00009377643,0.00009180974,0.0001170242,0.00001863901,0.00004861398,0.0001058962,0.00006814198,0.00002835592],"category_scores_gemma":[0.00001006952,0.00008128695,0.00006816099,0.0001862752,0.00002791752,0.00003067785,0.00005404518,0.00003985369,0.00001502741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002021022,"about_ca_system_score_gemma":0.0001209787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001311848,"about_ca_topic_score_gemma":0.00002117512,"domain_scores_codex":[0.999236,0.000019702,0.0003854448,0.000123941,0.0001121252,0.0001227739],"domain_scores_gemma":[0.9996505,0.000007166729,0.0000774322,0.000217231,0.00002486328,0.00002283976],"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":[0.0000355611,0.0000316597,0.00205867,0.0002447462,0.00005118385,8.687963e-7,0.000434295,0.0000446575,0.9294918,0.00140804,0.01654487,0.04965371],"study_design_scores_gemma":[0.0008837433,0.0001630459,0.02550371,0.0001097211,0.00004176866,0.0000111744,0.002004078,0.003379074,0.7214592,0.0002548822,0.245818,0.0003716112],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9778798,0.001451388,0.01710249,0.0002592163,0.0004824998,0.0003027924,0.00008126764,0.0000212977,0.002419263],"genre_scores_gemma":[0.997234,0.0004815616,0.001482311,0.0001529852,0.00004235552,0.00002358163,0.0002178146,0.00001121469,0.0003541387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2292732,"threshold_uncertainty_score":0.3314786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01587551541960474,"score_gpt":0.2952289495447525,"score_spread":0.2793534341251478,"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."}}