{"id":"W2795353351","doi":"10.1128/msystems.00195-17","title":"Developing a <i>Bacteroides</i> System for Function-Based Screening of DNA from the Human Gut Microbiome","year":2018,"lang":"en","type":"article","venue":"mSystems","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; University of Illinois at Urbana-Champaign; Government of Canada","keywords":"Bacteroides thetaiotaomicron; Metagenomics; Computational biology; Microbiome; Function (biology); Biology; Gut microbiome; Human Microbiome Project; Gene; DNA sequencing; Human microbiome; Annotation; Bacteroides fragilis; Bacteroides; DNA; Genetics; Bacteria","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":[0.00118505,0.0005018435,0.0005868716,0.0005677122,0.0004090893,0.0006774357,0.0005464455,0.0005500928,0.001440465],"category_scores_gemma":[0.0006632734,0.0002996669,0.0004564499,0.0003827248,0.0003388996,0.0004023579,0.0007803207,0.0008168373,0.001392828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003221937,"about_ca_system_score_gemma":0.0005323107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007453081,"about_ca_topic_score_gemma":0.0008906433,"domain_scores_codex":[0.9992918,0.000206011,0.00006101479,0.0001951323,0.0001707476,0.00007533724],"domain_scores_gemma":[0.9996861,0.00008383045,0.00006025661,0.00007217146,0.00003520249,0.0000624431],"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":[0.00005757771,0.00002607416,0.0002868515,0.00004034839,0.000005389588,0.00002583208,0.00001801062,0.0001210157,0.9958135,0.0002644599,0.0001383773,0.003202497],"study_design_scores_gemma":[0.00002099763,0.0002692692,0.001857251,0.00001988482,0.00002243646,0.000315993,0.00003137572,0.003218805,0.9881595,0.0002203668,0.005841603,0.00002249055],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5758629,0.001752402,0.4017475,0.001287365,0.0002391271,0.0008821787,0.005020864,0.008107805,0.005099853],"genre_scores_gemma":[0.6790479,0.00153039,0.3054746,0.0005951569,0.00004772029,0.0007075833,0.007131187,0.0004702153,0.004995088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001440465,"threshold_uncertainty_score":0.00626719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02600320082907076,"score_gpt":0.2683956389908012,"score_spread":0.2423924381617305,"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."}}