http://scholars.ntou.edu.tw/handle/123456789/19009
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zan, Anna | en_US |
dc.contributor.author | Xie, Zhong-Ru | en_US |
dc.contributor.author | Hsu, Yi-Chen | en_US |
dc.contributor.author | Chen, Yu-Hao | en_US |
dc.contributor.author | Lin, Tsung-Hsien | en_US |
dc.contributor.author | Chang, Yong-Shan | en_US |
dc.contributor.author | Chang, Kuan Y. | en_US |
dc.date.accessioned | 2021-12-09T06:21:46Z | - |
dc.date.available | 2021-12-09T06:21:46Z | - |
dc.date.issued | 2022-01 | - |
dc.identifier.issn | 0169-2607 | - |
dc.identifier.uri | http://scholars.ntou.edu.tw/handle/123456789/19009 | - |
dc.description.abstract | Background and Objective: Not everyone gets sick after an exposure to influenza A viruses (IAV). Al-though KLRD1 has been identified as a potential biomarker for influenza susceptibility, it remains un-clear whether forecasting symptomatic flu infection based on pre-exposure host gene expression might be possible. Method: To examine this hypothesis, we developed DeepFlu using the state-of-the-art deep learning ap-proach on the human gene expression data infected with IAV subtype H1N1 or H3N2 viruses to forecast who would catch the flu prior to an exposure to IAV. Results: The results indicated that such forecast is possible and, in other words, gene expression could reflect the strength of host immunity. In the leave-one-person-out cross-validation, DeepFlu based on deep neural network outperformed the models using convolutional neural network, random forest, or support vector machine, achieving 70.0% accuracy, 0.787 AUROC, and 0.758 AUPR for H1N1 and 73.8% accuracy, 0.847 AUROC, and 0.901 AUPR for H3N2. In the external validation, DeepFlu also reached 71.4% accuracy, 0.700 AUROC, and 0.723 AUPR for H1N1 and 73.5% accuracy, 0.732 AUROC, and 0.749 AUPR for H3N2, surpassing the KLRD1 biomarker. In addition, DeepFlu which was trained only by pre-exposure data worked the best than by other time spans and mixed training data of H1N1 and H3N2 did not necessarily enhance prediction. DeepFlu is available at https://github.com/ntou-compbio/DeepFlu . Conclusions: DeepFlu is a prognostic tool that can moderately recognize individuals susceptible to the flu and may help prevent the spread of IAV. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | ELSEVIER IRELAND LTD | en_US |
dc.relation.ispartof | COMPUT METH PROG BIO | en_US |
dc.subject | SEASONAL INFLUENZA | en_US |
dc.subject | IMMUNITY | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Influenza Prevention | en_US |
dc.subject | Influenza Susceptibility | en_US |
dc.title | DeepFlu: a deep learning approach for forecasting symptomatic influenza A infection based on pre-exposure gene expression | en_US |
dc.type | journal article | en_US |
dc.identifier.doi | 10.1016/j.cmpb.2021.106495 | - |
dc.identifier.isi | WOS:000720347300002 | - |
dc.relation.journalvolume | 213 | en_US |
item.cerifentitytype | Publications | - |
item.openairetype | journal article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.fulltext | no fulltext | - |
item.grantfulltext | none | - |
item.languageiso639-1 | en_US | - |
crisitem.author.dept | College of Electrical Engineering and Computer Science | - |
crisitem.author.dept | Department of Computer Science and Engineering | - |
crisitem.author.dept | National Taiwan Ocean University,NTOU | - |
crisitem.author.orcid | 0000-0002-2262-5218 | - |
crisitem.author.parentorg | National Taiwan Ocean University,NTOU | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
Appears in Collections: | 03 GOOD HEALTH AND WELL-BEING 資訊工程學系 14 LIFE BELOW WATER |
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