{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T07:06:40Z","timestamp":1776064000745,"version":"3.50.1"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T00:00:00Z","timestamp":1721692800000},"content-version":"vor","delay-in-days":22,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01EB031032"],"award-info":[{"award-number":["R01EB031032"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01AG056560"],"award-info":[{"award-number":["R01AG056560"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Arkansas Integrative Metabolic Research Center","award":["NIH P20GM139768"],"award-info":[{"award-number":["NIH P20GM139768"]}]},{"DOI":"10.13039\/100000057","name":"National Institutes of General Medical Sciences","doi-asserted-by":"crossref","award":["3P20GM103429-21S2"],"award-info":[{"award-number":["3P20GM103429-21S2"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>This manuscript describes the development of a resources module that is part of a learning platform named \u2018NIGMS Sandbox for Cloud-based Learning\u2019 https:\/\/github.com\/NIGMS\/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on implementing deep learning algorithms for biomedical image data in an interactive format that uses appropriate cloud resources for data access and analyses. Biomedical-related datasets are widely used in both research and clinical settings, but the ability for professionally trained clinicians and researchers to interpret datasets becomes difficult as the size and breadth of these datasets increases. Artificial intelligence, and specifically deep learning neural networks, have recently become an important tool in novel biomedical research. However, use is limited due to their computational requirements and confusion regarding different neural network architectures. The goal of this learning module is to introduce types of deep learning neural networks and cover practices that are commonly used in biomedical research. This module is subdivided into four submodules that cover classification, augmentation, segmentation and regression. Each complementary submodule was written on the Google Cloud Platform and contains detailed code and explanations, as well as quizzes and challenges to facilitate user training. Overall, the goal of this learning module is to enable users to identify and integrate the correct type of neural network with their data while highlighting the ease-of-use of cloud computing for implementing neural networks.<\/jats:p>\n               <jats:p>This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https:\/\/github.com\/NIGMS\/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.<\/jats:p>","DOI":"10.1093\/bib\/bbae232","type":"journal-article","created":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T13:43:41Z","timestamp":1721742221000},"source":"Crossref","is-referenced-by-count":7,"title":["Identifying and training deep learning neural networks on biomedical-related datasets"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8400-7813","authenticated-orcid":false,"given":"Alan E","family":"Woessner","sequence":"first","affiliation":[{"name":"University of Arkansas Arkansas Integrative Metabolic Research Center, , Fayetteville, AR"},{"name":"University of Arkansas Department of Biomedical Engineering, , Fayetteville, AR"}]},{"given":"Usman","family":"Anjum","sequence":"additional","affiliation":[{"name":"University of Arkansas Arkansas Integrative Metabolic Research Center, , Fayetteville, AR"},{"name":"University of Cincinnati Department of Computer Science, , Cincinnati, OH"},{"name":"University of Arkansas Department of Computer Science and Computer Engineering, , Fayetteville, AR"}]},{"given":"Hadi","family":"Salman","sequence":"additional","affiliation":[{"name":"University of Arkansas Arkansas Integrative Metabolic Research Center, , Fayetteville, AR"},{"name":"University of Arkansas Department of Computer Science and Computer Engineering, , Fayetteville, AR"}]},{"given":"Jacob","family":"Lear","sequence":"additional","affiliation":[{"name":"University of Arkansas Department of Computer Science and Computer Engineering, , Fayetteville, AR"}]},{"given":"Jeffrey T","family":"Turner","sequence":"additional","affiliation":[{"name":"Health Data and AI, Deloitte Consulting LLP , Arlington VA, USA"}]},{"given":"Ross","family":"Campbell","sequence":"additional","affiliation":[{"name":"Health Data and AI, Deloitte Consulting LLP , Arlington VA, USA"}]},{"given":"Laura","family":"Beaudry","sequence":"additional","affiliation":[{"name":"Google Cloud , Reston VA, USA"}]},{"given":"Justin","family":"Zhan","sequence":"additional","affiliation":[{"name":"University of Arkansas Arkansas Integrative Metabolic Research Center, , Fayetteville, AR"},{"name":"University of Cincinnati Department of Computer Science, , Cincinnati, OH"},{"name":"University of Arkansas Department of Computer Science and Computer Engineering, , Fayetteville, AR"}]},{"given":"Lawrence E","family":"Cornett","sequence":"additional","affiliation":[{"name":"University of Arkansas for Medical Sciences Department of Physiology and Cell Biology, , Little Rock, AR"}]},{"given":"Susan","family":"Gauch","sequence":"additional","affiliation":[{"name":"University of Arkansas Department of Computer Science and Computer Engineering, , Fayetteville, AR"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6876-3608","authenticated-orcid":false,"given":"Kyle P","family":"Quinn","sequence":"additional","affiliation":[{"name":"University of Arkansas Arkansas Integrative Metabolic Research Center, , Fayetteville, AR"},{"name":"University of Arkansas Department of Biomedical Engineering, , Fayetteville, AR"}]}],"member":"286","published-online":{"date-parts":[[2024,7,23]]},"reference":[{"key":"2024072312412313000_ref1","article-title":"NIGMS Sandbox: A Learning Platform toward Democratizing Cloud Computing for Biomedical Research","author":"Lei","journal-title":"Brief Bioinform"},{"key":"2024072312412313000_ref2","volume-title":"The Image Processing Handbook","author":"Russ","year":"2011"},{"key":"2024072312412313000_ref3","doi-asserted-by":"crossref","first-page":"A1","DOI":"10.1016\/j.mri.2019.12.006","article-title":"Artificial intelligence in medical imaging","volume":"68","author":"Gore","year":"2020","journal-title":"Magn Reson Imaging"},{"key":"2024072312412313000_ref4","article-title":"Deep learning in bioinformatics","volume":"18","author":"Min","journal-title":"Brief Bioinform"},{"key":"2024072312412313000_ref5","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1186\/s13073-021-00968-x","article-title":"Deep learning in cancer diagnosis, prognosis and treatment selection","volume":"13","author":"Tran","year":"2021","journal-title":"Genome Med"},{"key":"2024072312412313000_ref6","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pmed.1002730","article-title":"Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study","volume":"16","author":"Kather","year":"2019","journal-title":"PLoS Med"},{"key":"2024072312412313000_ref7","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J Big Data"},{"key":"2024072312412313000_ref8","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"2024072312412313000_ref9","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.1002\/lsm.23375","article-title":"Automated extraction of skin wound healing biomarkers from In vivo label-free multiphoton microscopy using convolutional neural networks","volume":"53","author":"Jones","year":"2021","journal-title":"Lasers Surg Med"},{"key":"2024072312412313000_ref10","doi-asserted-by":"crossref","DOI":"10.1002\/jbio.202200191","article-title":"Improved segmentation of collagen second harmonic generation images with a deep learning convolutional neural network","volume":"15","author":"Woessner","year":"2022","journal-title":"J Biophotonics"},{"key":"2024072312412313000_ref11","doi-asserted-by":"crossref","first-page":"9026","DOI":"10.1073\/pnas.1804420115","article-title":"Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D","volume":"115","author":"Newby","year":"2018","journal-title":"Proc Natl Acad Sci U S A"},{"key":"2024072312412313000_ref12","doi-asserted-by":"crossref","first-page":"86","DOI":"10.4258\/hir.2018.24.1.86","article-title":"Applying deep learning in medical images: the case of bone age estimation","volume":"24","author":"Lee","year":"2018","journal-title":"Healthc Inform Res"},{"key":"2024072312412313000_ref13","article-title":"Deep residual learning for image recognition","author":"He","journal-title":"arXiv e-prints 2015; arXiv:151203385"},{"key":"2024072312412313000_ref14","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun ACM"},{"key":"2024072312412313000_ref15","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","journal-title":"arXiv e-prints 2014; arXiv:14091556"},{"key":"2024072312412313000_ref16","volume-title":"2009 IEEE Conference on Computer Vision and Pattern Recognition","author":"Deng"},{"key":"2024072312412313000_ref17","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","article-title":"Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning","volume":"35","author":"Shin","year":"2016","journal-title":"IEEE Trans Med Imaging"},{"key":"2024072312412313000_ref18","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1038\/s41597-022-01721-8","article-title":"MedMNIST v2\u2014a large-scale lightweight benchmark for 2D and 3D biomedical image classification","volume":"10","author":"Yang","year":"2023","journal-title":"Sci Data"},{"key":"2024072312412313000_ref19","article-title":"Generalized cross entropy loss for training deep neural networks with noisy labels","author":"Zhang","journal-title":"arXiv e-prints 2018; arXiv:180507836"},{"key":"2024072312412313000_ref20","article-title":"The effectiveness of data augmentation in image classification using deep learning","author":"Perez","journal-title":"arXiv e-prints 2017; arXiv:171204621"},{"key":"2024072312412313000_ref21","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Wang","year":"2017"},{"key":"2024072312412313000_ref22","doi-asserted-by":"crossref","first-page":"104863","DOI":"10.1016\/j.dib.2019.104863","article-title":"Dataset of breast ultrasound images","volume":"28","author":"Al-Dhabyani","year":"2020","journal-title":"Data Brief"},{"key":"2024072312412313000_ref23","article-title":"Deep neural networks segment neuronal membranes in electron microscopy images","author":"Ciresan","year":"2012"},{"key":"2024072312412313000_ref24","article-title":"U-net: convolutional networks for biomedical image segmentation","author":"Ronneberger","journal-title":"arXiv e-prints 2015; arXiv:150504597"},{"key":"2024072312412313000_ref25","article-title":"You only learn one representation: unified network for multiple tasks","author":"Wang","journal-title":"arXiv e-prints 2021; arXiv:210504206"},{"key":"2024072312412313000_ref26","article-title":"Segment anything","author":"Kirillov","journal-title":"arXiv e-prints 2023; arXiv:230402643"},{"key":"2024072312412313000_ref27","author":"Klein"},{"key":"2024072312412313000_ref28","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1016\/j.jid.2020.10.010","article-title":"Automated quantitative analysis of wound histology using deep-learning neural networks","volume":"141","author":"Jones","year":"2021","journal-title":"J Investig Dermatol"},{"key":"2024072312412313000_ref29","article-title":"Adam: a method for stochastic optimization","author":"Kingma","journal-title":"arXiv e-prints 2014; arXiv:14126980"},{"key":"2024072312412313000_ref30","first-page":"861","volume-title":"Proc. SPIE 1905, Biomedical Image Processing and Biomedical Visualization","author":"Street"},{"key":"2024072312412313000_ref31","article-title":"Breast cancer Wisconsin (diagnostic)","author":"William, Wolberg","year":"1993"},{"key":"2024072312412313000_ref32","article-title":"Scikit-learn: machine learning in python","author":"Pedregosa","journal-title":"arXiv e-prints 2012; arXiv:12010490"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/Supplement_1\/bbae232\/58618828\/bbae232.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/Supplement_1\/bbae232\/58618828\/bbae232.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T13:44:04Z","timestamp":1721742244000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbae232\/7718482"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7]]},"references-count":32,"journal-issue":{"issue":"Supplement_1","published-print":{"date-parts":[[2024,7,23]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbae232","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,7]]},"published":{"date-parts":[[2024,7]]},"article-number":"bbae232"}}