{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T19:49:03Z","timestamp":1781812143750,"version":"3.54.5"},"reference-count":6,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T00:00:00Z","timestamp":1709683200000},"content-version":"vor","delay-in-days":5,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"German Federal Ministry of Education and Research"},{"name":"Innovation Partnership M2Aind, project M2Aind-DeepLearning","award":["13FH8I08IA"],"award-info":[{"award-number":["13FH8I08IA"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>Python is the most commonly used language for deep learning (DL). Existing Python packages for mass spectrometry imaging (MSI) data are not optimized for DL tasks. We, therefore, introduce pyM2aia, a Python package for MSI data analysis with a focus on memory-efficient handling, processing and convenient data-access for DL applications. pyM2aia provides interfaces to its parent application M2aia, which offers interactive capabilities for exploring and annotating MSI data in imzML format. pyM2aia utilizes the image input and output routines, data formats, and processing functions of M2aia, ensures data interchangeability, and enables the writing of readable and easy-to-maintain DL pipelines by providing batch generators for typical MSI data access strategies. We showcase the package in several examples, including imzML metadata parsing, signal processing, ion-image generation, and, in particular, DL model training and inference for spectrum-wise approaches, ion-image-based approaches, and approaches that use spectral and spatial information simultaneously.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Python package, code and examples are available at (https:\/\/m2aia.github.io\/m2aia)<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae133","type":"journal-article","created":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T11:46:00Z","timestamp":1709725560000},"source":"Crossref","is-referenced-by-count":7,"title":["pyM2aia: Python interface for mass spectrometry imaging with focus on deep learning"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3148-4282","authenticated-orcid":false,"given":"Jonas","family":"Cordes","sequence":"first","affiliation":[{"name":"Faculty of Computer Science, Mannheim University of Applied Sciences , Mannheim 68163, Germany"},{"name":"Medical Faculty Mannheim, Heidelberg University , Mannheim 68167, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1789-4090","authenticated-orcid":false,"given":"Thomas","family":"Enzlein","sequence":"additional","affiliation":[{"name":"Center for Mass Spectrometry and Optical Spectroscopy, Mannheim University of Applied Sciences , Mannheim 68163, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carsten","family":"Hopf","sequence":"additional","affiliation":[{"name":"Medical Faculty Mannheim, Heidelberg University , Mannheim 68167, Germany"},{"name":"Center for Mass Spectrometry and Optical Spectroscopy, Mannheim University of Applied Sciences , Mannheim 68163, Germany"},{"name":"Medical Faculty, Heidelberg University , Heidelberg 69120, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ivo","family":"Wolf","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Mannheim University of Applied Sciences , Mannheim 68163, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,3,5]]},"reference":[{"key":"2024031900353820900_btae133-B1","doi-asserted-by":"crossref","first-page":"5544","DOI":"10.1038\/s41467-021-25744-8","article-title":"Peak learning of mass spectrometry imaging data using artificial neural networks","volume":"12","author":"Abdelmoula","year":"2021","journal-title":"Nat Commun"},{"key":"2024031900353820900_btae133-B2","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1146\/annurev-biodatasci-011420-031537","article-title":"Spatial metabolomics and imaging mass spectrometry in the age of artificial intelligence","volume":"3","author":"Alexandrov","year":"2020","journal-title":"Annu Rev Biomed Data Sci"},{"key":"2024031900353820900_btae133-B3","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1021\/jasms.0c00393","article-title":"Batch effects in MALDI mass spectrometry imaging","volume":"32","author":"Balluff","year":"2021","journal-title":"J Am Soc Mass Spectrom"},{"key":"2024031900353820900_btae133-B4","doi-asserted-by":"crossref","first-page":"giab049","DOI":"10.1093\/gigascience\/giab049","article-title":"M2aia\u2014interactive, fast, and memory-efficient analysis of 2D and 3D multi-modal mass spectrometry imaging data","volume":"10","author":"Cordes","year":"2021","journal-title":"GigaScience"},{"key":"2024031900353820900_btae133-B5","doi-asserted-by":"crossref","first-page":"e2023773118","DOI":"10.1073\/pnas.2023773118","article-title":"Connecting structure and function from organisms to molecules in small-animal symbioses through chemo-histo-tomography","volume":"118","author":"Geier","year":"2021","journal-title":"Proc Natl Acad Sci U S A"},{"key":"2024031900353820900_btae133-B6","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1039\/D1SC04077D","article-title":"Self-supervised clustering of mass spectrometry imaging data using contrastive learning","volume":"13","author":"Hu","year":"2022","journal-title":"Chem Sci"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btae133\/56874497\/btae133.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/40\/3\/btae133\/57008505\/btae133.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/40\/3\/btae133\/57008505\/btae133.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T05:28:31Z","timestamp":1710826111000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btae133\/7623092"}},"subtitle":[],"editor":[{"given":"Janet","family":"Kelso","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2024,3,1]]},"references-count":6,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,3,4]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btae133","relation":{},"ISSN":["1367-4811"],"issn-type":[{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,3,1]]},"published":{"date-parts":[[2024,3,1]]},"article-number":"btae133"}}