Analyzing time series gene expression data
TLDR
This review is intended to serve as both, a point of reference for experimental biologists looking for practical solutions for analyzing their data, and a starting point for computer scientists interested in working on the computational problems related to time series expression analysis.Abstract:
Motivation: Time series expression experiments are an increasingly popular method for studying a wide range of biological systems. However, when analyzing these experiments researchers face many new computational challenges. Algorithms that are specifically designed for time series experiments are required so that we can take advantage of their unique features (such as the ability to infer causality from the temporal response pattern) and address the unique problems they raise (e.g. handling the different non-uniform sampling rates).
Results: We present a comprehensive review of the current research in time series expression data analysis. We divide the computational challenges into four analysis levels: experimental design, data analysis, pattern recognition and networks. For each of these levels, we discuss computational and biological problems at that level and point out some of the methods that have been proposed to deal with these issues. Many open problems in all these levels are discussed. This review is intended to serve as both, a point of reference for experimental biologists looking for practical solutions for analyzing their data, and a starting point for computer scientists interested in working on the computational problems related to time series expression analysis.read more
Citations
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Genetic regulatory networks
TL;DR: This paper aims to demonstrate the efforts towards in-situ applicability of EMMARM, which automates the very labor-intensive and therefore time-heavy and expensive and expensive process of manually cataloging and processing DNA sequences.
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Dynamics of gene expression and the regulatory inference problem
TL;DR: Stochastic models of gene expression dynamics are constructed and tested on experimental time series data of messenger-RNA concentrations to infer biophysical parameters of gene transcription, including the statistics of transcription factor-DNA binding and the target genes controlled by a given transcription factor.
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Conditional clustering of temporal expression profiles
TL;DR: This article presents a novel technique to cluster data from time course microarray experiments performed across several experimental conditions and uses the algorithm to characterize the response of human T cells to stimulations of antigen-receptor signaling gene expression temporal profiles measured in six different biological conditions.
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Dynamic Modelling and Prediction of Cytotoxicity on Microelectronic cell Sensor Array
Biao Huang,James Xing +1 more
TL;DR: It is shown that the black box modelling and first principle modelling both should be considered in challenging modelling problems such as the cytotoxicity, and two dynamic modelling approaches are considered, namely data-based system identification andFirst principle modelling.
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A patient-gene model for temporal expression profiles in clinical studies.
Naftali Kaminski,Ziv Bar-Joseph +1 more
TL;DR: A generative model is presented that represents patient expression data using two levels, a gene level, which corresponds to a common response pattern, and a patientlevel, which accounts for the patient specific expression patterns and response rate.
References
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Audrey P. Gasch,Paul T. Spellman,Camilla M. Kao,Orna Carmel-Harel,Michael B. Eisen,Gisela Storz,David Botstein,Patrick O. Brown +7 more
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Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays.
Uri Alon,Naama Barkai,Daniel A. Notterman,Kurt C. Gish,S. Ybarra,David H. Mack,A. J. Levine,A. J. Levine +7 more
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