{"id":"W2154348147","doi":"10.1093/bioinformatics/bts721","title":"Fragment recruitment on metabolic pathways: comparative metabolic profiling of metagenomes and metatranscriptomes","year":2013,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Leibniz-Gemeinschaft; Deutsche Forschungsgemeinschaft","keywords":"KEGG; Metagenomics; Profiling (computer programming); Perl; Metabolic pathway; Computational biology; Biology; Java; Computer science; Bioinformatics; Gene; Transcriptome; Genetics; World Wide Web; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00124607,0.0008164295,0.0006251786,0.001294416,0.0005826634,0.001141412,0.0006492012,0.0003795538,0.007965943],"category_scores_gemma":[0.001959248,0.0004228594,0.0008541047,0.002074851,0.0003242439,0.001007617,0.001623659,0.0009263165,0.00272421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005254416,"about_ca_system_score_gemma":0.0007587876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001250588,"about_ca_topic_score_gemma":0.001758192,"domain_scores_codex":[0.9993631,0.0001012007,0.00003397373,0.0002697925,0.0001748482,0.00005700305],"domain_scores_gemma":[0.9991574,0.0003043477,0.0001096713,0.0001648564,0.0001334679,0.0001302106],"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":[0.001947753,0.0001703759,0.01452753,0.001643476,0.0001700277,0.0003086424,0.0005036534,0.004528998,0.8152093,0.003606631,0.01761753,0.1397661],"study_design_scores_gemma":[0.0001803228,0.0004811863,0.05644336,0.0002049246,0.0001789822,0.0008359146,0.000298308,0.06187592,0.7929044,0.008936934,0.07750779,0.0001519676],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3213379,0.001670865,0.5000729,0.0008817635,0.0001904622,0.0005143307,0.1002276,0.06132464,0.01377957],"genre_scores_gemma":[0.3723029,0.00116718,0.5272436,0.0004048502,0.00006391443,0.0009560796,0.08585501,0.007457194,0.004549176],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007965943,"threshold_uncertainty_score":0.02664876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04910625613312687,"score_gpt":0.2687357045996056,"score_spread":0.2196294484664787,"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."}}