Name
Title | ||
|---|---|---|
Inferring Causal Relations from Multivariate Time Series: A Fast Method for Large-Scale Gene Expression Data |
Abstract | ||
|---|---|---|
Various multivariate time series analysis techniques have been developed with the aim of inferring causal relations between time series. Previously, these techniques have proved their effectiveness on economic and neurophysiological data, which normally consist of hundreds of samples. However, in their applications to gene regulatory inference, the small sample size of gene expression time series poses an obstacle. In this paper, we describe some of the most commonly used multivariate inference techniques and show the potential challenge related to gene expression analysis. In response, we propose a directed partial correlation (DPC) algorithm as an efficient and effective solution to causal/regulatory relations inference on small sample gene expression data. Comparative evaluations on the existing techniques and the proposed method are presented. To draw reliable conclusions, a comprehensive benchmarking on data sets of various setups is essential. Three experiments are designed to assess these methods in a coherent manner. Detailed analysis of experimental results not only reveals good accuracy of the proposed DPC method in large-scale prediction, but also gives much insight into all methods under evaluation. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/BIBE.2009.8 | BIBE |
Keywords | DocType | ISSN |
neurophysiological data,analysis technique,inference mechanisms,genetics,directed partial correlation algorithm,genomics,gene expression,large-scale gene expression data,gene regulatory inference,inferring causal relations,fast method,multivariate time series,multivariate inference technique,casual relations,detailed analysis,microarray,inferring causal relation,small sample gene expression,genomic research,gene expression analysis,regulatory relations inference,bioinformatics,time series,data set benchmarking,gene expression time series,data models,economic forecasting,covariance matrix,correlation,computer science,time series analysis,computational modeling,biomedical engineering,partial correlation,time measurement | Conference | 2471-7819 |
ISBN | Citations | PageRank |
978-0-7695-3656-9 | 0 | 0.34 |
References | Authors | |
7 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yinyin Yuan | 1 | 62 | 5.38 |
| Chang-Tsun Li | 2 | 937 | 72.14 |