NDLI: Inferring Causal Relations from Multivariate Time Series: A Fast Method for Large-Scale Gene Expression Data

Content Provider	IEEE Xplore Digital Library
Author	Yinyin Yuan Chang-Tsun Li
Copyright Year	2009
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 ef¿cient 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.
Starting Page	92
Ending Page	99
File Size	299008
Page Count	8
File Format	PDF
ISBN	9780769536569
DOI	10.1109/BIBE.2009.8
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-22
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Large-scale systems Gene expression Bioinformatics Economic forecasting Time measurement Time series analysis Genomics Biomedical engineering Computer science Inference algorithms microarray time series casual relations gene expression
Content Type	Text
Resource Type	Article

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