Optimal Gene Regulatory Network Inference using the Boolean Kalman Filter and Multiple Model Adaptive Estimation

被引：0

作者：

Imani, Mahdi ^{[1
]}

Braga-Neto, Ulisses ^{[1
]}

机构：

[1] Texas A&M Univ, Dept Elect & Comp Engn, College Stn, TX 77843 USA

来源：

2015 49TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS | 2015年

关键词：

Gene Regulatory Network; Boolean Kalman Filter; Adaptive Filtering; Network Inference;

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

We propose a method for the inference of Boolean gene regulatory networks observed through noise. The algorithm is based on the optimal MMSE state estimator for a Boolean dynamical system, known as the Boolean Kalman filter (BKF). In the presence of partial knowledge about the network, a bank of BKFs representing the candidate models is run in parallel in a framework known as Multiple Model Adaptive Estimation (MMAE). Performance is investigated using a model of the p53-MDM2 negative feedback loop network, as well as application to large numbers of random networks in order to estimate average performance.

引用

页码：423 / 427

页数：5