Motivation The reconstruction of gene regulatory networks (GRNs) from genes profiles has a
growing interest in bioinformatics for understanding the complex regulatory mechanisms in …

Gene regulatory network (GRN) is produced as a result of regulatory interactions between
different genes through their coded proteins in cellular context. Having immense importance …

Gene regulatory networks (GRNs) participate in many biological processes, and
reconstructing them plays an important role in systems biology. Although many advanced …

Reconstruction of the Gene Regulatory Network (GRN) is important in understanding the
functionalities of a living cell. The reconstruction of GRN is based on microarray gene …

Utilizing gene expression data to infer gene regulatory networks has received great attention
because gene regulation networks can reveal complex life phenomena by studying the …

Reconstructing gene regulatory networks based on time-series gene expression data is a
huge challenge in the field of systems biology. However, the accuracy of traditional methods …

基因之间的调控关系隐含在基因表达数据里, 需要分析该数据从而揭示基因调控网络的拓扑结构
. 由于静态基因表达数据的样本较少, 因此本文提出基于距离相关性扩充样本数据量的方法 …

Deep learning methods have found increasing interest in recent years because of their wide
applicability for prediction and inference in numerous disciplines such as image recognition …

Gene regulatory network (GRN) plays a pivotal role in cells. Existing high-throughput
experiments facilitate abundant time-series expression data to reconstruct GRN to gain …

Background: Time series expression data of genes contain relations among different genes,
which are difficult to model precisely. Slime-forming bacteria is one of the three major …