{"id":"W3214527858","doi":"10.33137/utjph.v2i2.36763","title":"Constructing Long Short-Term Memory Networks to Predict Ulcerative Colitis Progression from Longitudinal Gut Microbiome Profiles","year":2021,"lang":"en","type":"article","venue":"University of Toronto Journal of Public Health","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Public Health Ontario; University of Toronto","funders":"","keywords":"Autoencoder; Microbiome; Computer science; Artificial intelligence; Encoder; Construct (python library); Deep learning; Machine learning; Bioinformatics; Biology","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":[0.0006454045,0.0001454155,0.0003856481,0.00004845123,0.0002117647,0.00003326684,0.000263747,0.0001513425,0.0002378255],"category_scores_gemma":[0.00005728487,0.0001557549,0.0001375757,0.0000704231,0.0001211274,0.00004101765,0.0001921142,0.0001796909,7.837367e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004461602,"about_ca_system_score_gemma":0.001826517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001226584,"about_ca_topic_score_gemma":0.0143431,"domain_scores_codex":[0.9984058,0.0003560486,0.0003978188,0.0002767866,0.0001637338,0.0003997734],"domain_scores_gemma":[0.9981806,0.00002402732,0.000431972,0.000206484,0.0006569402,0.0005000061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001372227,0.001098555,0.4217862,0.0005515577,0.001271212,0.0005919765,0.00755943,0.00009363827,0.263256,0.0002281365,0.03646834,0.2657228],"study_design_scores_gemma":[0.003211206,0.002708293,0.9440396,0.001027688,0.00008939786,0.0009146782,0.0207011,0.00008018724,0.01122137,0.000006203256,0.01544304,0.0005572593],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9773757,0.003183731,0.01649927,0.001742063,0.0003822944,0.0002471913,0.0001596201,0.000006904778,0.0004032203],"genre_scores_gemma":[0.9882504,0.0009829977,0.009811472,0.0001698848,0.0003649563,1.671945e-7,0.0001998047,0.00001242898,0.0002079005],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5222534,"threshold_uncertainty_score":0.8003787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02191452799849488,"score_gpt":0.2789392072551017,"score_spread":0.2570246792566069,"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."}}