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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Book ChapterDOI
Gene Regulatory Network Identification from Gene Expression Time Series Data Using Swarm Intelligence
TL;DR: Computer simulation reveals that the proposed realization of GRN by a recurrent fuzzy neural network outperforms the most recently reported work in this field in detecting the sign and magnitude and also the structure of the overall network.
A novel bit freezing technique to improve gene specific co-regulation discovery from gene expression databases
Ji Zhang,Yonglong Luo +1 more
TL;DR: A novel technique to improve the performance of gene specific co-regulation discovery using a bit freezing approach that can contribute to fast crossover and mutation operations, achieve an early stop of the GA and facilitate the construction of kNN Search Table Plus (kNN-ST+) that leads to more accurate approximation of fitness function.
Proceedings ArticleDOI
Cluster analysis of microarray data of Shewanella oneidensis
S. Nilufar,M. Ara +1 more
TL;DR: This paper proposes to cluster the temporal gene expression data of Shewanella oneidensis to define its molecular response at different time intervals following acidic pH and basic pH exposure and to find a relation of these temporal data at different environmental conditions.
Gene Network Inference and Expression Prediction Using Recurrent Neural Networks and Evolutionary Algorithms
TL;DR: An improved validation framework for the evaluation of genetic network modeling systems that will result in better generalization and long-term prediction capability is provided.
Journal ArticleDOI
A personalised approach for identifying disease-relevant pathways in heterogeneous diseases
TL;DR: This work presents a method that models time-course data in a personalised manner using Gaussian processes in order to identify differentially expressed genes (DEGs) and combines the DEG lists on a pathway-level using a permutation-based empirical hypothesis testing to overcome gene-level variability and inconsistencies prevalent to datasets from heterogeneous diseases.
References
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Journal ArticleDOI
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.
Journal ArticleDOI
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.
Todd R. Golub,Todd R. Golub,Donna K. Slonim,Pablo Tamayo,Christine Huard,Michelle Gaasenbeek,Jill P. Mesirov,Hilary A. Coller,Mignon L. Loh,James R. Downing,Michael A. Caligiuri,Clara D. Bloomfield,Eric S. Lander +12 more
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.
Journal ArticleDOI
Comprehensive Identification of Cell Cycle–regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization
Paul T. Spellman,Gavin Sherlock,Gavin Sherlock,Michael Q. Zhang,Vishwanath R. Iyer,Kirk R. Anders,Michael B. Eisen,Patrick O. Brown,Patrick O. Brown,David Botstein,Bruce Futcher +10 more
TL;DR: A comprehensive catalog of yeast genes whose transcript levels vary periodically within the cell cycle is created, and it is found that the mRNA levels of more than half of these 800 genes respond to one or both of these cyclins.
Journal ArticleDOI
Genomic expression programs in the response of yeast cells to environmental changes.
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.
Journal ArticleDOI
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.