Inferring Causal Gene Targets from Time Course Expression Data. Issue 26 (2016)

• Record Type: Journal Article
• Authors: Noh, Heeju; Ziyi, Hua; Gunawan, Rudiyanto
• Is Part Of: IFAC-PapersOnLine. Volume 49:Issue 26(2016)
• Page Start: 350
• Page End: 356
• Publication Date: 2016
• Subjects: time series -- gene expression -- network perturbations -- mechanism of action -- Lasso; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2016.12.151
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: In this work, we proposed a network analysis method called DeltaNeTS for inferring genetic perturbations from temporal transcriptional expression profiles. While several efficacious and robust network analysis methods exist for steady state data, a direct application of these methods to analyze time series expression data often leads to a poor prediction performance. DeltaNeTS is an extension of our previous method DeltaNet, which involves a single-step inference of gene regulatory network and gene targets from transcriptional profiles. In order to prevent reversals in the causal directions of gene regulations when analyzing time series data, DeltaNeTS employs an additional constraint based on the time derivatives of the temporal expression profiles. We demonstrated the advantages of DeltaNeTS over DeltaNet and a network analysis method called Time Series Network Inference (TSNI), by analyzing time-series expression data from in silico simulations and from microarray assays of Saccharomyces cerevisiae (yeast) and cultured human airway epithelial cells.