可解释性
坐标下降
弹性网正则化
计算机科学
Lasso(编程语言)
逻辑回归
回归
数据挖掘
机器学习
基因调控网络
人工智能
算法
数学
统计
特征选择
万维网
基因表达
基因
化学
生物化学
作者
Wenwen Min,Juan Liu,Shihua Zhang
标识
DOI:10.1109/tcbb.2016.2640303
摘要
Molecular profiling data (e.g., gene expression) has been used for clinical risk prediction and biomarker discovery. However, it is necessary to integrate other prior knowledge like biological pathways or gene interaction networks to improve the predictive ability and biological interpretability of biomarkers. Here, we first introduce a general regularized Logistic Regression (LR) framework with regularized term , which can reduce to different penalties, including Lasso, elastic net, and network-regularized terms with different . This framework can be easily solved in a unified manner by a cyclic coordinate descent algorithm which can avoid inverse matrix operation and accelerate the computing speed. However, if those estimated and have opposite signs, then the traditional network-regularized penalty may not perform well. To address it, we introduce a novel network-regularized sparse LR model with a new penalty to consider the difference between the absolute values of the coefficients. We develop two efficient algorithms to solve it. Finally, we test our methods and compare them with the related ones using simulated and real data to show their efficiency.
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