Prediction and Analysis of Key Genes in Glioblastoma Based on Bioinformatics
Joint Authors
Song, Ye
Long, Hao
Liang, Chaofeng
Zhang, Xi’an
Fang, Luxiong
Wang, Gang
Qi, Songtao
Huo, Haizhong
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-01-16
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Understanding the mechanisms of glioblastoma at the molecular and structural level is not only interesting for basic science but also valuable for biotechnological application, such as the clinical treatment.
In the present study, bioinformatics analysis was performed to reveal and identify the key genes of glioblastoma multiforme (GBM).
The results obtained in the present study signified the importance of some genes, such as COL3A1, FN1, and MMP9, for glioblastoma.
Based on the selected genes, a prediction model was built, which achieved 94.4% prediction accuracy.
These findings might provide more insights into the genetic basis of glioblastoma.
American Psychological Association (APA)
Long, Hao& Liang, Chaofeng& Zhang, Xi’an& Fang, Luxiong& Wang, Gang& Qi, Songtao…[et al.]. 2017. Prediction and Analysis of Key Genes in Glioblastoma Based on Bioinformatics. BioMed Research International،Vol. 2017, no. 2017, pp.1-7.
https://search.emarefa.net/detail/BIM-1138693
Modern Language Association (MLA)
Long, Hao…[et al.]. Prediction and Analysis of Key Genes in Glioblastoma Based on Bioinformatics. BioMed Research International No. 2017 (2017), pp.1-7.
https://search.emarefa.net/detail/BIM-1138693
American Medical Association (AMA)
Long, Hao& Liang, Chaofeng& Zhang, Xi’an& Fang, Luxiong& Wang, Gang& Qi, Songtao…[et al.]. Prediction and Analysis of Key Genes in Glioblastoma Based on Bioinformatics. BioMed Research International. 2017. Vol. 2017, no. 2017, pp.1-7.
https://search.emarefa.net/detail/BIM-1138693
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138693