ridm@nrct.go.th   ระบบคลังข้อมูลงานวิจัยไทย   รายการโปรดที่คุณเลือกไว้

Integration of multiple data sources to prioritize candidate genes using discounted rating system

หน่วยงาน Nanyang Technological University, Singapore

รายละเอียด

ชื่อเรื่อง : Integration of multiple data sources to prioritize candidate genes using discounted rating system
นักวิจัย : Li, Yongjin , Patra, Jagdish Chandra
คำค้น : DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences.
หน่วยงาน : Nanyang Technological University, Singapore
ผู้ร่วมงาน : -
ปีพิมพ์ : 2553
อ้างอิง : Li, Y., & Patra, J. C. (2010). Integration of multiple data sources to prioritize candidate genes using discounted rating system. BMC Bioinformatics, 11(Suppl 1), S20. , 1471-2105 , http://hdl.handle.net/10220/7087 , http://dx.doi.org/10.1186/1471-2105-11-S1-S20 , 152418
ที่มา : -
ความเชี่ยวชาญ : -
ความสัมพันธ์ : BMC bioinformatics
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

Identifying disease gene from a list of candidate genes is an important task in bioinformatics. The main strategy is to prioritize candidate genes based on their similarity to known disease genes. Most of existing gene prioritization methods access only one genomic data source, which is noisy and incomplete. Thus, there is a need for the integration of multiple data sources containing different information. In this paper, we proposed a combination strategy, called discounted rating system (DRS). We performed leave one out cross validation to compare it with N-dimensional order statistics (NDOS) used in Endeavour. Results showed that the AUC (Area Under the Curve) values achieved by DRS were comparable with NDOS on most of the disease families. But DRS worked much faster than NDOS, especially when the number of data sources increases. When there are 100 candidate genes and 20 data sources, DRS works more than 180 times faster than NDOS. In the framework of DRS, we give different weights for different data sources. The weighted DRS achieved significantly higher AUC values than NDOS. The proposed DRS algorithm is a powerful and effective framework for candidate gene prioritization. If weights of different data sources are proper given, the DRS algorithm will perform better.

บรรณานุกรม :
Li, Yongjin , Patra, Jagdish Chandra . (2553). Integration of multiple data sources to prioritize candidate genes using discounted rating system.
    กรุงเทพมหานคร : Nanyang Technological University, Singapore.
Li, Yongjin , Patra, Jagdish Chandra . 2553. "Integration of multiple data sources to prioritize candidate genes using discounted rating system".
    กรุงเทพมหานคร : Nanyang Technological University, Singapore.
Li, Yongjin , Patra, Jagdish Chandra . "Integration of multiple data sources to prioritize candidate genes using discounted rating system."
    กรุงเทพมหานคร : Nanyang Technological University, Singapore, 2553. Print.
Li, Yongjin , Patra, Jagdish Chandra . Integration of multiple data sources to prioritize candidate genes using discounted rating system. กรุงเทพมหานคร : Nanyang Technological University, Singapore; 2553.