Regression-Based Network Estimation for High-Dimensional Genetic Data

Journal of computational biology: A journal of computational molecular cell biology

Sung WonHan^4,1¹  JunheeSeok^3,2²  MinhyeokLee^2³  Kyu MinLee^1⁴ 

[1] Address correspondence to: Dr. Sung Won Han, School of Industrial Management Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, South Korea^4;Prof. Junhee Seok, School of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, South Korea^3;School of Electrical Engineering, Korea University, Seoul, South Korea^2;School of Industrial Management Engineering, Korea University, Seoul, South Korea^1

关键词: adaptive elastic-net;    gene network estimation;    graphical model;    regression-based approach;   

DOI  :  10.1089/cmb.2018.0225

学科分类：生物科学（综合）

来源: Mary Ann Liebert, Inc. Publishers

【 摘 要 】

Given the continuous advancement in genome sequencing technology, large volumes of gene expression data can be easily obtained. However, the corresponding increase in genetic information necessitates adoption of a new approach for network estimation. Data dimensions increase with the progress in genome sequencing technology, thereby making it difficult to estimate gene networks by causing multicollinearity. Furthermore, such a problem also occurs when hub nodes exist, where gene networks are known to have regulator genes that can be interpreted as hub nodes. This study aims at developing methods that demonstrate good performance when handling high-dimensional data with hub nodes. We propose regression-based approaches as feasible solutions in this article. Elastic-net and adaptive elastic-net penalty regressions were applied to compensate for the disadvantages of existing regression-based approaches employing LASSO or adaptive LASSO. Experiments were performed to compare the proposed regression-based approaches with other conventional methods. We confirmed the superior performance of the regression-based approaches and applied it to actual genetic data to verify the suitability to estimate gene networks. As results, robustness of the proposed methods was demonstrated with respect to high-dimensional gene expression data.

【 授权许可】

Unknown