Development of an Early Prediction Model for Subarachnoid Hemorrhage With Genetic and Signaling Pathway Analysis

Wanjing Lei; Han Zeng; Hua Feng; Xufang Ru; Qiang Li; Ming Xiao; Huiru Zheng; Yujie Chen; Le Zhang

doi:10.3389/fgene.2020.00391

Development of an Early Prediction Model for Subarachnoid Hemorrhage With Genetic and Signaling Pathway Analysis

Wanjing Lei
, Han Zeng
, Hua Feng
, Xufang Ru
, Qiang Li
, Ming Xiao
, Huiru Zheng
, Yujie Chen
, Le Zhang

Research output: Contribution to journal › Article › peer-review

8 Citations (Scopus)

231 Downloads (Pure)

Abstract

Subarachnoid hemorrhage (SAH) is devastating disease with high mortality, high disability rate, and poor clinical prognosis. It has drawn great attentions in both basic and clinical medicine. Therefore, it is necessary to explore the therapeutic drugs and effective targets for early prediction of SAH. Firstly, we demonstrate that LCN2 can effectively intervene or treat SAH from the perspective of cell signaling pathway. Next, three potential genes that we explored have been validated by manually reviewed experimental evidences. Finally, we turn out that the SAH early ensemble learning predictive model performs better than the classical LR, SVM, and Naïve-Bayes models.

Original language	English
Article number	391
Journal	Frontiers in Genetics
Volume	11
DOIs	https://doi.org/10.3389/fgene.2020.00391
Publication status	Published (in print/issue) - 21 Apr 2020

Bibliographical note

Funding Information:
This work has been supported in part by the National Science and Technology Major Innovation Program (No. 2018ZX10201002) and supported by the National Natural Science Foundation of China (No. 61372138), State Key Laboratory of Trauma, Burn and Combined Injury (No. SKLRCJF01), and Chongqing Talent Program (No. 4139Z2391).

Publisher Copyright:
© Copyright © 2020 Lei, Zeng, Feng, Ru, Li, Xiao, Zheng, Chen and Zhang.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Funding

Funding Information: This work has been supported in part by the National Science and Technology Major Innovation Program (No. 2018ZX10201002) and supported by the National Natural Science Foundation of China (No. 61372138), State Key Laboratory of Trauma, Burn and Combined Injury (No. SKLRCJF01), and Chongqing Talent Program (No. 4139Z2391). Publisher Copyright: © Copyright © 2020 Lei, Zeng, Feng, Ru, Li, Xiao, Zheng, Chen and Zhang. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

artificial intelligence
big data
bioinformatics
genetics
genomics

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503036_Chen_Manuscript_SubmittedAuthor Accepted Manuscript, 1.61 MB
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Cite this

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title = "Development of an Early Prediction Model for Subarachnoid Hemorrhage With Genetic and Signaling Pathway Analysis",

abstract = "Subarachnoid hemorrhage (SAH) is devastating disease with high mortality, high disability rate, and poor clinical prognosis. It has drawn great attentions in both basic and clinical medicine. Therefore, it is necessary to explore the therapeutic drugs and effective targets for early prediction of SAH. Firstly, we demonstrate that LCN2 can effectively intervene or treat SAH from the perspective of cell signaling pathway. Next, three potential genes that we explored have been validated by manually reviewed experimental evidences. Finally, we turn out that the SAH early ensemble learning predictive model performs better than the classical LR, SVM, and Na{\"i}ve-Bayes models.",

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note = "Funding Information: This work has been supported in part by the National Science and Technology Major Innovation Program (No. 2018ZX10201002) and supported by the National Natural Science Foundation of China (No. 61372138), State Key Laboratory of Trauma, Burn and Combined Injury (No. SKLRCJF01), and Chongqing Talent Program (No. 4139Z2391). Publisher Copyright: {\textcopyright} Copyright {\textcopyright} 2020 Lei, Zeng, Feng, Ru, Li, Xiao, Zheng, Chen and Zhang. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",

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AU - Lei, Wanjing

AU - Zeng, Han

AU - Feng, Hua

AU - Ru, Xufang

AU - Li, Qiang

AU - Xiao, Ming

AU - Zheng, Huiru

AU - Chen, Yujie

AU - Zhang, Le

N1 - Funding Information: This work has been supported in part by the National Science and Technology Major Innovation Program (No. 2018ZX10201002) and supported by the National Natural Science Foundation of China (No. 61372138), State Key Laboratory of Trauma, Burn and Combined Injury (No. SKLRCJF01), and Chongqing Talent Program (No. 4139Z2391). Publisher Copyright: © Copyright © 2020 Lei, Zeng, Feng, Ru, Li, Xiao, Zheng, Chen and Zhang. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/4/21

Y1 - 2020/4/21

N2 - Subarachnoid hemorrhage (SAH) is devastating disease with high mortality, high disability rate, and poor clinical prognosis. It has drawn great attentions in both basic and clinical medicine. Therefore, it is necessary to explore the therapeutic drugs and effective targets for early prediction of SAH. Firstly, we demonstrate that LCN2 can effectively intervene or treat SAH from the perspective of cell signaling pathway. Next, three potential genes that we explored have been validated by manually reviewed experimental evidences. Finally, we turn out that the SAH early ensemble learning predictive model performs better than the classical LR, SVM, and Naïve-Bayes models.

AB - Subarachnoid hemorrhage (SAH) is devastating disease with high mortality, high disability rate, and poor clinical prognosis. It has drawn great attentions in both basic and clinical medicine. Therefore, it is necessary to explore the therapeutic drugs and effective targets for early prediction of SAH. Firstly, we demonstrate that LCN2 can effectively intervene or treat SAH from the perspective of cell signaling pathway. Next, three potential genes that we explored have been validated by manually reviewed experimental evidences. Finally, we turn out that the SAH early ensemble learning predictive model performs better than the classical LR, SVM, and Naïve-Bayes models.

KW - artificial intelligence

KW - big data

KW - bioinformatics

KW - genetics

KW - genomics

UR - https://www.scopus.com/pages/publications/85084268137

UR - https://www.frontiersin.org/article/10.3389/fgene.2020.00391/full

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DO - 10.3389/fgene.2020.00391

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VL - 11

JO - Frontiers in Genetics

JF - Frontiers in Genetics

M1 - 391

ER -

Development of an Early Prediction Model for Subarachnoid Hemorrhage With Genetic and Signaling Pathway Analysis

Abstract

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

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