Paper Info
Title
Inference of genetic regulatory network for stem cell using single cells expression data
Abstract
Single cell experimental studies provide an unprecedented opportunity to examine the heterogeneity of molecular processes in different cells. However, the reconstruction of a sequence of changes in molecular processes and development of regulatory networks using single cell data are still challenging problems in bioinformatics and systems biology. In this work we propose an integrated framework to infer genetic regulatory networks using single cell experimental data. We first use the Wanderlust algorithm to construct the pseudo-trajectory of gene expression activities. Due to noise in the expression data, a Gauss process regression method is employed to produce a smoothly trajectory. Our integrated approach includes both a top-down approach (i.e. the GENIE3 algorithm) to infer the network structure and a bottom-up approach (i.e. differential equation model) to reverse-engineering the regulatory network. Using the gene network of hematopoietic development in the mouse embryo as the test problem, we developed a dynamic model for a network of nine genes. Our results suggest that the proposed integrated framework is an effective approach to reconstruct regulatory networks from single cell data.
Year
DOI
Venue
2016
10.1109/BIBM.2016.7822521
2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Keywords
Field
DocType
gene regulatory network,single cell data,network inference,stem cell
Data mining,Data modeling,Gene,Experimental data,Computer science,Artificial intelligence,Data visualization,Regression,Inference,Systems biology,Bioinformatics,Gene regulatory network,Machine learning
Conference
ISSN
ISBN
Citations 
2156-1125
978-1-5090-1612-9
0
PageRank 
References 
Authors
0.34
8
4
Name
Order
Citations
PageRank
Jiangyong Wei101.35
Xiaohua Hu22819314.15
Xiufen Zou327225.44
Tianhai Tian416930.29