Deep Learning for Integrated Analysis of Insulin Resistance with Multi-Omics Data
1
Department of Advanced Green Energy and Environment, Handong Global University, Pohang-si, Gyeongbuk 37554, Korea
2
Department of Life Science, Handong Global University, Pohang-si, Gyeonbuk 37554, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Caroline Heckman
J. Pers. Med. 2021, 11(2), 128; https://doi.org/10.3390/jpm11020128
Received: 8 January 2021
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Revised: 25 January 2021
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Accepted: 10 February 2021
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Published: 15 February 2021
(This article belongs to the Special Issue Use of Clinical Decision Support Software within Health Care Systems)
Technological advances in next-generation sequencing (NGS) have made it possible to uncover extensive and dynamic alterations in diverse molecular components and biological pathways across healthy and diseased conditions. Large amounts of multi-omics data originating from emerging NGS experiments require feature engineering, which is a crucial step in the process of predictive modeling. The underlying relationship among multi-omics features in terms of insulin resistance is not well understood. In this study, using the multi-omics data of type II diabetes from the Integrative Human Microbiome Project, from 10,783 features, we conducted a data analytic approach to elucidate the relationship between insulin resistance and multi-omics features, including microbiome data. To better explain the impact of microbiome features on insulin classification, we used a developed deep neural network interpretation algorithm for each microbiome feature’s contribution to the discriminative model output in the samples.
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MDPI and ACS Style
Huang, E.; Kim, S.; Ahn, T. Deep Learning for Integrated Analysis of Insulin Resistance with Multi-Omics Data. J. Pers. Med. 2021, 11, 128. https://doi.org/10.3390/jpm11020128
AMA Style
Huang E, Kim S, Ahn T. Deep Learning for Integrated Analysis of Insulin Resistance with Multi-Omics Data. Journal of Personalized Medicine. 2021; 11(2):128. https://doi.org/10.3390/jpm11020128
Chicago/Turabian StyleHuang, Eunchong, Sarah Kim, and TaeJin Ahn. 2021. "Deep Learning for Integrated Analysis of Insulin Resistance with Multi-Omics Data" Journal of Personalized Medicine 11, no. 2: 128. https://doi.org/10.3390/jpm11020128
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