{"id":"W3135298367","doi":"10.1101/2021.03.08.433891","title":"immuneML: an ecosystem for machine learning analysis of adaptive immune receptor repertoires","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Norges Forskningsråd; Stiftelsen Kristian Gerhard Jebsen; National Institutes of Health; Leona M. and Harry B. Helmsley Charitable Trust","keywords":"Benchmarking; Computer science; Interpretability; Workflow; Interoperability; Transparency (behavior); Data mining; Machine learning; Database; World Wide Web","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000830921,0.0005428448,0.001073329,0.0003212446,0.0001819602,0.0001494772,0.0006467363,0.000631726,0.00003069899],"category_scores_gemma":[0.0003055259,0.0005589943,0.0007170444,0.0005322744,0.00004908259,0.00002228431,0.0006582303,0.0004093987,0.000001724872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007267439,"about_ca_system_score_gemma":0.00038143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001447616,"about_ca_topic_score_gemma":0.0000282941,"domain_scores_codex":[0.9972756,0.0001933878,0.001029816,0.0008387562,0.0002301717,0.0004322938],"domain_scores_gemma":[0.9962878,0.00003487748,0.001015191,0.001658041,0.0008714576,0.0001326822],"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":[0.0001864888,0.0001671112,0.0018654,0.0004298042,0.003754036,0.000001468793,0.00004330858,0.003222812,0.9902194,0.00007464861,0.00002631476,0.000009249125],"study_design_scores_gemma":[0.0007849241,0.0005021414,0.011649,0.000251405,0.00156519,2.75592e-8,0.0001443931,0.04319091,0.9372205,2.208068e-7,0.003751203,0.0009400764],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9844507,0.007390861,0.006311283,0.00002744489,0.0003881861,0.0006448642,0.0007206519,0.00006006919,0.00000593661],"genre_scores_gemma":[0.985563,0.0009924866,0.01278899,0.00002512777,0.0001759913,0.0002320808,0.00008937449,0.0001164394,0.00001655711],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05299884,"threshold_uncertainty_score":0.9996862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01332751377571232,"score_gpt":0.2165416635426985,"score_spread":0.2032141497669862,"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."}}