{"id":"W3211434666","doi":"10.1038/s42256-021-00413-z","title":"The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires","year":2021,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of Allergy and Infectious Diseases; Norges Forskningsråd; Illinois Nutrient Research and Education Council; Stiftelsen Kristian Gerhard Jebsen; National Institutes of Health; Leona M. and Harry B. Helmsley Charitable Trust","keywords":"Computer science; Benchmarking; Workflow; Interpretability; Interoperability; Machine learning; Data mining; Data science; Artificial intelligence; World Wide Web; Database","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":[0.0005582178,0.0002096367,0.0003601894,0.00007775615,0.0002736881,0.00004827702,0.0004410633,0.0002565111,0.00003072643],"category_scores_gemma":[0.0008123936,0.0001443143,0.0004597615,0.0005194352,0.00004384579,0.00000712389,0.0002217905,0.0004067204,0.000002440979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001877449,"about_ca_system_score_gemma":0.00007295485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004889922,"about_ca_topic_score_gemma":0.000262018,"domain_scores_codex":[0.9985948,0.00009960125,0.0005923919,0.00029647,0.0001673525,0.0002494222],"domain_scores_gemma":[0.9984264,0.0001675556,0.0003330595,0.0006059107,0.0004292309,0.00003785346],"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":[0.002318752,0.0004121201,0.004756693,0.0003737203,0.01286672,0.000005221843,0.001264469,0.02827452,0.7526894,0.01511854,0.001304815,0.180615],"study_design_scores_gemma":[0.000221569,0.0003741328,0.000584909,0.00004046457,0.0004138636,0.00001550444,0.0009245062,0.1152944,0.7858016,0.000113273,0.09590756,0.0003081451],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.473648,0.3307598,0.1861922,0.001697774,0.001616464,0.001497332,0.0009733809,0.00008229316,0.003532693],"genre_scores_gemma":[0.9922255,0.002803531,0.002703411,0.00005913989,0.00006936751,0.00003634586,0.0008340036,0.00002314046,0.001245531],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5185775,"threshold_uncertainty_score":0.5884965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008216227075964772,"score_gpt":0.2522911315569597,"score_spread":0.2440749044809949,"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."}}