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      DeepInsight: A methodology to transform a non-image data to an image for convolution neural network architecture

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          Abstract

          It 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.

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          Most cited references6

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          The random subspace method for constructing decision forests

          Tin Ho (1998)
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            DeepChrome: deep-learning for predicting gene expression from histone modifications

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              Very deep convolutional networks for large-scale image recognition

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                Author and article information

                Contributors
                alok.fj@gmail.com
                tsunoda.mesm@mri.tmd.ac.jp
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                6 August 2019
                6 August 2019
                2019
                : 9
                : 11399
                Affiliations
                [1 ]Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
                [2 ]ISNI 0000 0004 0437 5432, GRID grid.1022.1, Institute for Integrated and Intelligent Systems, , Griffith University, ; Brisbane, Australia
                [3 ]ISNI 0000 0001 2171 4027, GRID grid.33998.38, School of Engineering & Physics, , University of the South Pacific, ; Suva, Fiji
                [4 ]ISNI 0000 0004 1754 9200, GRID grid.419082.6, CREST, JST, ; Tokyo, Japan
                [5 ]ISNI 0000 0004 1791 9005, GRID grid.419257.c, Division of Genomic Medicine, Medical Genome Center, , National Center for Geriatrics and Gerontology, ; Obu, Aichi Japan
                [6 ]ISNI 0000 0001 1014 9130, GRID grid.265073.5, Department of Medical Science Mathematics, Medical Research Institute, , Tokyo Medical and Dental University, ; Tokyo, Japan
                [7 ]ISNI 0000 0001 2151 536X, GRID grid.26999.3d, Laboratory for Medical Science Mathematics, Department of Biological Sciences, Graduate School of Science, , The University of Tokyo, ; Tokyo, Japan
                [8 ]ISNI 0000 0004 0455 8044, GRID grid.417863.f, School of Electrical and Electronics Engineering, , Fiji National University, ; Suva, Fiji
                Author information
                http://orcid.org/0000-0002-7668-3501
                http://orcid.org/0000-0003-4225-0879
                http://orcid.org/0000-0001-7095-7332
                http://orcid.org/0000-0002-5439-7918
                Article
                47765
                10.1038/s41598-019-47765-6
                6684600
                31388036
                168e0792-66de-40b3-9c6f-a858de35dfd4
                © The Author(s) 2019

                Open Access 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. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 6 February 2019
                : 22 July 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100003382, MEXT | JST | Core Research for Evolutional Science and Technology (CREST);
                Award ID: JPMJCR 1412
                Award ID: JPMJCR 1412
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001691, MEXT | Japan Society for the Promotion of Science (JSPS);
                Award ID: 17H06307
                Award ID: 17H06299
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                bioinformatics,computational models,computational science
                Uncategorized
                bioinformatics, computational models, computational science

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