{"id":"W2925176230","doi":"10.1101/585802","title":"A generic multivariate framework for the integration of microbiome longitudinal studies with other data types","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"National Health and Medical Research Council; Medical Research Council","keywords":"Microbiome; DECIPHER; Multivariate statistics; Profiling (computer programming); Compositional data; Smoothing; Computer science; Data type; Data mining; Data integration; Data science; Dimensionality reduction; Metagenomics; Cyberinfrastructure; Data exploration; Biology; Bioinformatics; Machine learning; Visualization","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":[0.02944053,0.002117364,0.001921735,0.004826398,0.0009812425,0.004773104,0.004491468,0.001972019,0.00341171],"category_scores_gemma":[0.03567266,0.001432895,0.006430338,0.004499228,0.001745322,0.003065048,0.008061972,0.003834454,0.001311302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001859181,"about_ca_system_score_gemma":0.004666987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01138528,"about_ca_topic_score_gemma":0.0145302,"domain_scores_codex":[0.9886031,0.006270061,0.0011233,0.001623611,0.001969066,0.000410745],"domain_scores_gemma":[0.9817051,0.01015613,0.001792047,0.002702141,0.00280594,0.0008386123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003062993,0.0003281233,0.01390971,0.001027215,0.00171278,0.001394417,0.000867357,0.3507185,0.005930153,0.4702717,0.009337197,0.1441966],"study_design_scores_gemma":[0.00003540411,0.00007586756,0.001731036,0.0001311139,0.0001261489,0.000227755,0.00009262701,0.7944548,0.0008802642,0.1880024,0.01415804,0.00008458957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004986125,0.0001185026,0.9981604,0.0001671323,0.00001987635,0.00005975973,0.0003908249,0.000416233,0.0001685359],"genre_scores_gemma":[0.03133021,0.0004732117,0.9642465,0.0002911186,0.0001489832,0.0006302348,0.00184813,0.0002775183,0.0007540844],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02944053,"threshold_uncertainty_score":0.1556982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05651058539854006,"score_gpt":0.2967782261151,"score_spread":0.2402676407165599,"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."}}