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dc.contributor.authorSharma, Alok
dc.contributor.authorVans, Edwin
dc.contributor.authorShigemizu, Daichi
dc.contributor.authorBoroevich, Keith A
dc.contributor.authorTsunoda, Tatsuhiko
dc.date.accessioned2019-12-16T04:53:50Z
dc.date.available2019-12-16T04:53:50Z
dc.date.issued2019
dc.identifier.issn2045-2322
dc.identifier.doi10.1038/s41598-019-47765-6
dc.identifier.urihttp://hdl.handle.net/10072/389806
dc.description.abstractIt is critical, but difficult, to catch the small variation in genomic or other kinds of data that differentiates phenotypes or categories. A plethora of data is available, but the information from its genes or elements is spread over arbitrarily, making it challenging to extract relevant details for identification. However, an arrangement of similar genes into clusters makes these differences more accessible and allows for robust identification of hidden mechanisms (e.g. pathways) than dealing with elements individually. Here we propose, DeepInsight, which converts non-image samples into a well-organized image-form. Thereby, the power of convolution neural network (CNN), including GPU utilization, can be realized for non-image samples. Furthermore, DeepInsight enables feature extraction through the application of CNN for non-image samples to seize imperative information and shown promising results. To our knowledge, this is the first work to apply CNN simultaneously on different kinds of non-image datasets: RNA-seq, vowels, text, and artificial.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherNature Publishing Group
dc.relation.ispartofissue1
dc.relation.ispartofjournalScientific Reports
dc.relation.ispartofvolume9
dc.subject.fieldofresearchNanotechnology
dc.subject.fieldofresearchcode1007
dc.subject.keywordsScience & Technology
dc.subject.keywordsMultidisciplinary Sciences
dc.subject.keywordsScience & Technology - Other Topics
dc.subject.keywordsLINEAR DISCRIMINANT-ANALYSIS
dc.titleDeepInsight: A methodology to transform a non-image data to an image for convolution neural network architecture
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationSharma, A; Vans, E; Shigemizu, D; Boroevich, KA; Tsunoda, T, DeepInsight: A methodology to transform a non-image data to an image for convolution neural network architecture, Scientific Reports, 2019, 9 (1)
dcterms.dateAccepted2019-07-22
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.date.updated2019-12-16T04:51:52Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© The Author(s) 2019. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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gro.griffith.authorSharma, Alok


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