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 journalArticlepeer-review

6 Citations (Scopus)
163 Downloads (Pure)


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 languageEnglish
Article number391
JournalFrontiers in Genetics
Publication statusPublished (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 2020 Elsevier B.V., All rights reserved.


  • artificial intelligence
  • big data
  • bioinformatics
  • genetics
  • genomics


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