{"id":"W2097185378","doi":"10.1093/bioinformatics/btr392","title":"MetATT: a web-based metabolomics tool for analyzing time-series and two-factor datasets","year":2011,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Institute for Nanotechnology; University of Alberta","funders":"Genome Alberta; Alberta Innovates","keywords":"Principal component analysis; Computer science; Metabolomics; Visualization; Data mining; Multivariate statistics; Time series; Session (web analytics); Identification (biology); Series (stratigraphy); Bioinformatics; Machine learning; Artificial intelligence; Biology","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":[0.0002522142,0.0002206681,0.0003023588,0.00009369531,0.0001364619,0.00004664846,0.0001737858,0.00008665216,0.00003516717],"category_scores_gemma":[0.0001787194,0.0001867022,0.0001070448,0.00009531919,0.0001037767,0.00002324359,0.000141493,0.0000543462,0.00001160002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007187118,"about_ca_system_score_gemma":0.00006635238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003957078,"about_ca_topic_score_gemma":0.00001142735,"domain_scores_codex":[0.9990397,0.00001884918,0.0003589999,0.0002011329,0.00008897473,0.0002923141],"domain_scores_gemma":[0.9992766,0.0000241087,0.0001731649,0.0003753582,0.00007352289,0.00007725722],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003409073,0.0007456147,0.01891654,0.001655182,0.005107567,0.0000103143,0.001805377,0.00006438144,0.8043844,0.0160717,0.0731578,0.07467211],"study_design_scores_gemma":[0.005272293,0.001376623,0.003492191,0.00002637769,0.0006946296,0.00002801101,0.0002791202,0.01087693,0.4429701,0.0004609878,0.5331256,0.001397116],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9271654,0.00411905,0.0575817,0.0002045422,0.0005195542,0.001455672,0.006563038,0.00009823954,0.002292726],"genre_scores_gemma":[0.3146499,0.001705681,0.6790772,0.0009580319,0.0003227913,0.0001445074,0.002479534,0.00007628428,0.0005860844],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6214955,"threshold_uncertainty_score":0.7613496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01831110162887262,"score_gpt":0.2474766800421787,"score_spread":0.229165578413306,"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."}}