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 journalArticle

1 Citation (Scopus)
22 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 languageEnglish
Article number391
JournalFrontiers in Genetics
Volume11
DOIs
Publication statusPublished - 21 Apr 2020

Keywords

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

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