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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Exploring Correlation Network for Cheating Detection
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Measurement variation determines the gene network topology reconstructed from experimental data: a case study of the yeast cyclin network
Cuong To,Jiri Vohradsky +1 more
TL;DR: This work analyzed static genome‐wide location data together with microarray kinetic measurements using a recurrent neural network‐based model of gene expression and a newly developed, unbiased algorithm based on evolutionary programming principles to identify cyclin genetic networks that were active during the yeast cell cycle.
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Bayesian models for two-sample time-course microarray experiments
TL;DR: The proposed procedure successfully deals with various technical difficulties which arise in microarray time-course experiments, such as small number of observations, non-uniform sampling intervals and presence of missing data or repeated measurements, thus offering a good compromise between nonparametric and normality assumption based techniques.
Journal ArticleDOI
Semi-supervised network inference using simulated gene expression dynamics.
Phan Nguyen,Rosemary Braun +1 more
TL;DR: A semi-supervised network reconstruction algorithm that enables the synthesis of information from partially known networks with time course gene expression data and is able to recover many of the true edges and identify errors in these networks, suggesting its ability to derive posterior networks that accurately reflect gene expression dynamics.
Bioinformatics original paper
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References
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Cluster analysis and display of genome-wide expression patterns
TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.
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TL;DR: A generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case and suggests a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
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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
TL;DR: Analysis of genomic expression patterns in the yeast Saccharomyces cerevisiae implicated the transcription factors Yap1p, as well as Msn2p and Msn4p, in mediating specific features of the transcriptional response, while the identification of novel sequence elements provided clues to novel regulators.
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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
TL;DR: In this paper, a two-way clustering algorithm was applied to both the genes and the tissues, revealing broad coherent patterns that suggest a high degree of organization underlying gene expression in these tissues.