{"id":"W3112662112","doi":"10.1038/s42256-020-00232-8","title":"Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions","year":2020,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":84,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lockheed Martin (Canada); National Research Council Canada","funders":"Stanford Maternal and Child Health Research Institute; National Institute of Dental and Craniofacial Research; National Institute of Allergy and Infectious Diseases; National Institute of Neurological Disorders and Stroke; National Institute of General Medical Sciences; National Heart, Lung, and Blood Institute; National Institute on Aging; Burroughs Wellcome Fund; U.S. Food and Drug Administration; American Heart Association; National Institutes of Health; U.S. Department of Health and Human Services; March of Dimes Foundation; Hamilton Health Sciences Foundation; Bill and Melinda Gates Foundation; Robertson Foundation; Doris Duke Charitable Foundation","keywords":"Mass cytometry; Computer science; Overfitting; Machine learning; Artificial intelligence; Profiling (computer programming); Immune system; Immunology; Medicine; Artificial neural network; 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":[0.0001441466,0.0002089918,0.0002168437,0.00004859738,0.0001013723,0.00001803211,0.0003143027,0.0003844627,0.00004635176],"category_scores_gemma":[0.0007947307,0.0001716271,0.0001314824,0.0002253364,0.0001002107,0.00000592719,0.0001135763,0.0008329669,0.00001041404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001520366,"about_ca_system_score_gemma":0.00004951645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001078122,"about_ca_topic_score_gemma":0.0001447638,"domain_scores_codex":[0.9988927,0.00008733421,0.0003496635,0.0003793075,0.0001218655,0.0001690945],"domain_scores_gemma":[0.9993942,0.00003868884,0.0001108356,0.0001843695,0.0001845728,0.00008737212],"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":[0.0002435442,0.000120367,0.000185853,0.0000431284,0.00003466506,0.000001576314,0.000302531,0.000269283,0.9591072,0.001057578,0.00005980572,0.03857445],"study_design_scores_gemma":[0.0001996825,0.00113077,0.0001233225,0.00003179799,0.00004245515,0.00001027361,0.00009610911,0.1069165,0.8795039,0.000521169,0.01120234,0.0002216219],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07664562,0.01424645,0.9064364,0.0007392276,0.0004732158,0.0002864211,0.00005485933,0.00008507592,0.001032667],"genre_scores_gemma":[0.9948078,0.0006690955,0.003467909,0.0002449988,0.0002467348,0.00001064403,0.0003174772,0.00002288279,0.0002124547],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9181622,"threshold_uncertainty_score":0.6998751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0137246454107532,"score_gpt":0.2678726170943851,"score_spread":0.2541479716836319,"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."}}