A greedy-network-based approach for human disease module identification
- 10.2991/iceeecs-16.2016.98How to use a DOI?
- Gene expression, Biological networks, Greedy algorithm, Machine learning, Cancer biology
The accurate classification of disease module from gene expression profiles is quite challenging for new biomarkers because of high noise in gene expression measurements and the small sample size . Studies have shown that network-based gene selection is more reliable than individual genes. Because genes related with same or similar disease modules usually reside in the same vicinity of the molecular network . Based on this theory, we propose a greedy-network-based approach for gene identification. In our study, we use this method in a pediatric acute lymphoblastic leukemia (ALL)  dataset and a triple-negative breast cancer (TNBC) microarray dataset. The results show our method achieves higher accuracy in the identification of gene makers.
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Meng Jin AU - Zhiyuan Yang AU - Jianwei Lu AU - Tianwei Yu PY - 2016/12 DA - 2016/12 TI - A greedy-network-based approach for human disease module identification BT - Proceedings of the 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016) PB - Atlantis Press SP - 474 EP - 478 SN - 2352-538X UR - https://doi.org/10.2991/iceeecs-16.2016.98 DO - 10.2991/iceeecs-16.2016.98 ID - Jin2016/12 ER -