{"id":"W2889339419","doi":"10.1093/bioinformatics/bty780","title":"Co-registration and analysis of multiple imaging mass spectrometry datasets targeting different analytes","year":2018,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Workflow; Mass spectrometry imaging; Source code; Sample (material); Data mining; Multivariate statistics; R package; Analyte; Code (set theory); Pattern recognition (psychology); Mass spectrometry; Artificial intelligence; Database; Machine learning; Computational science; Chemistry; Programming language","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":[0.009965042,0.002442874,0.002199904,0.006249617,0.001535753,0.004482301,0.003373485,0.001609995,0.004290444],"category_scores_gemma":[0.01860941,0.001209791,0.002971913,0.006273846,0.001421536,0.002943969,0.006298444,0.002393218,0.006594822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009454961,"about_ca_system_score_gemma":0.002797484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002328607,"about_ca_topic_score_gemma":0.004441351,"domain_scores_codex":[0.9924552,0.001290323,0.0007757764,0.002929755,0.002171999,0.0003768925],"domain_scores_gemma":[0.9909188,0.002372616,0.00138576,0.002980815,0.002013517,0.0003286324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002841563,0.0007908486,0.05643102,0.006417712,0.004786862,0.002062667,0.002095182,0.04238911,0.3384185,0.01785325,0.1188673,0.4070461],"study_design_scores_gemma":[0.0003181297,0.0004219489,0.05605179,0.000536588,0.00161998,0.004661573,0.001132771,0.2694035,0.4142722,0.05446829,0.1964186,0.0006945799],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05765411,0.002221514,0.8611079,0.0014107,0.000453123,0.0005844399,0.02630999,0.04564857,0.004609674],"genre_scores_gemma":[0.09643242,0.0008157832,0.8478668,0.0005372603,0.000196439,0.0008215842,0.04444253,0.006726198,0.002161005],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009965042,"threshold_uncertainty_score":0.05270082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01183538028323795,"score_gpt":0.2730484600134204,"score_spread":0.2612130797301825,"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."}}