{"id":"W3175203932","doi":"10.1093/bioinformatics/btab482","title":"phyLoSTM: a novel deep learning model on disease prediction from longitudinal microbiome data","year":2021,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto; Princess Margaret Cancer Centre; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada; Leona M. and Harry B. Helmsley Charitable Trust","keywords":"Microbiome; Computer science; Artificial intelligence; Machine learning; Random forest; Convolutional neural network; Longitudinal study; Deep learning; Bioinformatics; Biology; Medicine; Pathology","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":[0.001680407,0.000978909,0.000819134,0.00084177,0.0003231092,0.0006843393,0.00188275,0.001275884,0.001995289],"category_scores_gemma":[0.003406638,0.0004657653,0.0009829833,0.0007315289,0.0004197333,0.0008959127,0.001330343,0.001700264,0.0005502389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000964214,"about_ca_system_score_gemma":0.001471239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009186469,"about_ca_topic_score_gemma":0.009598367,"domain_scores_codex":[0.9997359,0.00008796382,0.00001798257,0.00007764839,0.00003827329,0.00004217432],"domain_scores_gemma":[0.9992747,0.0004154751,0.00008191995,0.00004790988,0.0001242723,0.00005573098],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006268136,0.0002402024,0.01706976,0.0001832241,0.0003089393,0.0002394056,0.00006617855,0.7773853,0.001540852,0.003166459,0.01003639,0.1891364],"study_design_scores_gemma":[0.00001108308,0.00002444708,0.0002226258,0.000008102573,0.00001032218,0.00001481092,0.00000279583,0.9976766,0.0002218588,0.001523574,0.0002801203,0.000003643892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1107915,0.002148555,0.8705028,0.002595424,0.0002828844,0.0001481973,0.004537523,0.007256576,0.001736446],"genre_scores_gemma":[0.7839704,0.0009343458,0.2021604,0.001148959,0.0002222559,0.0005578399,0.006080192,0.0002370767,0.004688692],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009186469,"threshold_uncertainty_score":0.01826602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03789715023382904,"score_gpt":0.2732261782664164,"score_spread":0.2353290280325874,"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."}}