{"id":"W3156857223","doi":"10.1371/journal.pcbi.1008887","title":"MAUI (MBI Analysis User Interface)—An image processing pipeline for Multiplexed Mass Based Imaging","year":2021,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Division of Chemistry; National Science Foundation; Damon Runyon Cancer Research Foundation; Azrieli Foundation; Canadian Institutes of Health Research; National Institute on Aging; Council for Higher Education; Bill and Melinda Gates Foundation; European Commission; National Institutes of Health; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Cancer Institute","keywords":"Computer science; Graphical user interface; Pipeline (software); Multiplexing; Mass cytometry; Interface (matter); Software; Image processing; Computer vision; Image (mathematics); Chemistry; Phenotype","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":[0.0001173728,0.000178557,0.0002481027,0.00009926651,0.0001197777,0.00005340769,0.0001590772,0.0001138774,0.00004530937],"category_scores_gemma":[0.0001074813,0.0001801013,0.0001784979,0.0001983413,0.00009580334,0.000009278269,0.00003529138,0.00007847108,0.000003847258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001833586,"about_ca_system_score_gemma":0.0001621512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009938859,"about_ca_topic_score_gemma":0.00004006647,"domain_scores_codex":[0.9987274,0.0001049328,0.0003004259,0.0005354029,0.00007979519,0.0002520537],"domain_scores_gemma":[0.9990674,0.00006680338,0.00009664393,0.0001805356,0.0005105814,0.00007798274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001597452,0.0002147429,0.01105132,0.00003639307,0.0001886153,0.000002939635,0.00002668597,0.0235952,0.9621382,0.00004751777,0.00009574402,0.002442948],"study_design_scores_gemma":[0.001136717,0.00008304048,0.0009172559,0.000009477812,0.0001891018,0.000003598649,0.00004310474,0.6995831,0.2957645,0.0005100095,0.001514079,0.0002459656],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2555676,0.0002490964,0.743327,0.0003963705,0.00008407761,0.000131504,0.000156638,0.00002763271,0.00006001562],"genre_scores_gemma":[0.8616642,0.00000365814,0.1324342,0.0007356426,0.0001675681,0.00002820719,0.004843624,0.00002457897,0.00009829865],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.675988,"threshold_uncertainty_score":0.734432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01877066446158277,"score_gpt":0.2854966483857032,"score_spread":0.2667259839241204,"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."}}