{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002169751,0.002625957,0.0009688433,0.001872475,0.0006090292,0.001677291,0.003132859,0.0009743506,0.05254506],"category_scores_gemma":[0.005501943,0.001486153,0.001210101,0.0007730662,0.0003894362,0.001601942,0.00217905,0.002162637,0.0246586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006926493,"about_ca_system_score_gemma":0.001190753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002786088,"about_ca_topic_score_gemma":0.002439277,"domain_scores_codex":[0.9994391,0.00009365429,0.00006387148,0.0001304867,0.0001965308,0.00007629406],"domain_scores_gemma":[0.9986726,0.0005396368,0.00009257286,0.0001808414,0.0003890329,0.0001252066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001517912,0.0002142561,0.004868587,0.001357304,0.0004101492,0.0006144428,0.0008617931,0.005495031,0.07037991,0.01075673,0.6683172,0.2352066],"study_design_scores_gemma":[0.000440842,0.00022924,0.006220662,0.0003337974,0.000179706,0.0007557347,0.0001171111,0.2760226,0.1657256,0.01156843,0.5379773,0.0004290243],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003195385,0.0002750256,0.5076762,0.0002673783,0.0001714253,0.0003574945,0.01308017,0.4714483,0.003528531],"genre_scores_gemma":[0.05924363,0.0006398392,0.7973306,0.001046322,0.0001858568,0.004398081,0.03387014,0.09001958,0.01326609],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05254506,"threshold_uncertainty_score":0.1757807,"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."}}