Biological Network Modeling Based on Hill Function and Hybrid Evolutionary Algorithm

• 作者: Haifeng Wang Sanrong Liu
• 来源期刊: 国际计算机前沿大会会议论文集
• 学科: 社会科学
• 关键词: Gene REGULATORY network HILL FUNCTION GREY WOLF optimization Hybrid EVOLUTIONARY algorithm Ordinary differential equation
• 年，卷（期）: 2019,（2）
• 页码范围: 192-194
• 页数: 3页
• 分类号: C

摘要

Gene regulatory network inference helps understand the regulatory mechanism among genes, predict the functions of unknown genes, comprehend the pathogenesis of disease and speed up drug development. In this paper, a Hill function-based ordinary differential equation (ODE) model is proposed to infer gene regulatory network (GRN). A hybrid evolutionary algorithm based on binary grey wolf optimization (BGWO) and grey wolf optimization (GWO) is proposed to identify the structure and parameters of the Hill function-based model. In order to restrict the search space and eliminate the redundant regulatory relationships, L1 regularizer was added to the fitness function. SOS repair network was used to test the proposed method. The experimental results show that this method can infer gene regulatory network more accurately than state of the art methods.