Abstract
Personalized medicine is usually based on known subcategories of a disease for better treatment. Identifying biomarkers that predict disease subtypes has been an important topic in biomédical sciences. There is a controversy as to the optimal number of genes as an input of a feature selection algorithm. In this paper, we investigate the feasibility to use genes pre-selected by biological knowledge rather than all available genes as an input for a feature selection algorithm predicting survival in the glioblastoma of the The Cancer Genome Atlas (TCGA). We discuss the advantage and disadvantage of this approach.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2012 |
| Pages | 872-875 |
| Number of pages | 4 |
| DOIs | |
| State | Published - 2012 |
| Externally published | Yes |
| Event | 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2012 - Philadelphia, PA, United States Duration: Oct 4 2012 → Oct 7 2012 |
Publication series
| Name | Proceedings - 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2012 |
|---|
Conference
| Conference | 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2012 |
|---|---|
| Country/Territory | United States |
| City | Philadelphia, PA |
| Period | 10/4/12 → 10/7/12 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 3 Good Health and Well-being
Keywords
- brain
- cancer
- gene expression
- survival
Fingerprint
Dive into the research topics of 'Predicting survial by cancer pathway gene expression profiles in the TCGA'. Together they form a unique fingerprint.Cite this
- APA
- Standard
- Harvard
- Vancouver
- Author
- BIBTEX
- RIS