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

Scientific Reports
Alok SharmaTatsuhiko Tsunoda

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.

Citations

Nov 17, 2020·Briefings in Bioinformatics·Haiying WangHuiru Zheng
Dec 8, 2020·Briefings in Bioinformatics·Edwin VansAlok Sharma
Nov 3, 2020·Computational and Structural Biotechnology Journal·Yotsawat PomyenSakda Khoomrung
Apr 6, 2021·PeerJ. Computer Science·Shiu KumarAlok Sharma
Apr 22, 2021·Journal of Gastroenterology and Hepatology·Tao ZengZhangran Chen
Jun 4, 2021·BMC Bioinformatics·Shiu KumarAlok Sharma
Sep 19, 2020·Analytical Biochemistry·Ronesh SharmaAlok Sharma
Aug 10, 2021·Briefings in Bioinformatics·Alok SharmaTatsuhiko Tsunoda
Sep 10, 2021·Acta Neuropathologica·David R AmiciMarc L Mendillo
Oct 5, 2021··Abena Achiaa AtwereboannahEbenezer Nanor

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Methods Mentioned

BETA
feature extraction
RNA-seq
PCA

Software Mentioned

DELVE
DeepInsight

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