Thyroid Cancer Gene Detection Algorithm Based on Feature Selection ELM
Authors
Jining Wang, HaoYang Mi, Yubo Wu, Xingtong Li, Chaohui Lin
Corresponding Author
Jining Wang
Available Online April 2016.
- DOI
- 10.2991/ameii-16.2016.262How to use a DOI?
- Keywords
- Extreme Learning Machine, Gene Expression Data, Correlation Analysis
- Abstract
At present, the detection and diagnosis of thyroid cancer has always been depend on the puncture cytology of thyroid gland. However, this method (the puncture cytology of thyroid gland) demands high accuracy of the instrument and costs may stay at a relative high level. Under this circumstance, aiming at solving this problem, our team comes up with a method, which is the thyroid cancer gene detection algorithm based on feature, to assist medical detection. The experiment of 100 genetic samples from DDBJ human gene bank shows that, the thyroid cancer gene detection algorithm based on feature selection ELM method can effectively improve the result of the thyroid cancer detection.
- Copyright
- © 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 - Jining Wang AU - HaoYang Mi AU - Yubo Wu AU - Xingtong Li AU - Chaohui Lin PY - 2016/04 DA - 2016/04 TI - Thyroid Cancer Gene Detection Algorithm Based on Feature Selection ELM BT - Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016) PB - Atlantis Press SN - 2352-5401 UR - https://doi.org/10.2991/ameii-16.2016.262 DO - 10.2991/ameii-16.2016.262 ID - Wang2016/04 ER -