共查询到20条相似文献，搜索用时 15 毫秒
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
Background
Inference of gene regulatory networks is a key goal in the quest for understanding fundamental cellular processes and revealing underlying relations among genes. With the availability of gene expression data, computational methods aiming at regulatory networks reconstruction are facing challenges posed by the data's high dimensionality, temporal dynamics or measurement noise. We propose an approach based on a novel multi-layer evolutionary trained neuro-fuzzy recurrent network (ENFRN) that is able to select potential regulators of target genes and describe their regulation type. 相似文献14.
15.
16.
17.
Chen Wang Jianhua Xuan Huai Li Yue Wang Ming Zhan Eric P Hoffman Robert Clarke 《BMC bioinformatics》2010,11(1):162
Background
In cancer, gene networks and pathways often exhibit dynamic behavior, particularly during the process of carcinogenesis. Thus, it is important to prioritize those genes that are strongly associated with the functionality of a network. Traditional statistical methods are often inept to identify biologically relevant member genes, motivating researchers to incorporate biological knowledge into gene ranking methods. However, current integration strategies are often heuristic and fail to incorporate fully the true interplay between biological knowledge and gene expression data. 相似文献18.
19.