Abstract
In this paper, we employ fuzzy techniques for analysing cancer microarray gene expression data. A fuzzy-based ensemble model and a comprehensive fuzzy-based framework for cancer microarray data analysis are being proposed. Our methods were tested on three benchmark microarray cancer data sets, namely Leukemia Cancer Data Set, Colon Cancer Data Set and Lymphoma Cancer Data Set. Comparing to other traditional statistical and machine learning models, the new approach can efficiently tackle several key problems in cancer microarray gene expression data analysis, including highly correlated genes, high dimensionality, highly noisy data and further explanation of the diagnosis results.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE |
| Publisher | IEEE |
| Pages | 1003-1010 |
| Number of pages | 8 |
| ISBN (Print) | 1424415098, 9781424415090 |
| DOIs | |
| Publication status | Published - 2007 |
| Externally published | Yes |
| Event | 7th IEEE International Conference on Bioinformatics and Bioengineering - Boston, United States Duration: 14 Jan 2007 → 17 Jan 2007 |
Conference
| Conference | 7th IEEE International Conference on Bioinformatics and Bioengineering |
|---|---|
| Abbreviated title | BIBE |
| Country | United States |
| City | Boston |
| Period | 14/01/07 → 17/01/07 |
ASJC Scopus subject areas
- Biotechnology
- Genetics
- Bioengineering
Fingerprint Dive into the research topics of 'A comprehensive fuzzy-based framework for cancer microarray data gene expression analysis'. Together they form a unique fingerprint.
Cite this
Wang, Z., & Palade, V. (2007). A comprehensive fuzzy-based framework for cancer microarray data gene expression analysis. In Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE (pp. 1003-1010). [4375680] IEEE. https://doi.org/10.1109/BIBE.2007.4375680