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
Identifying Non-random Patterns from Gene Expression Profiles
TL;DR: The results presented elucidate that ZCC can perform better than LZ in identifying biologically relevant genes and the biological relevance of new genes identified using ZCC not previously reported is discussed.
Book ChapterDOI
A Random Approach to Study the Stability of Fuzzy Logic Networks
TL;DR: The simulation results show that the fuzzy logic network's logical function causes the system to be on the edge of chaos when the number of nodes increases, and this logical function is more useful to infer real complex networks, such as gene regulatory networks.
Posted Content
Triclustering of Gene Expression Microarray data using Evolutionary Approach
Shreya Mishra,Swati Vipsita +1 more
TL;DR: In this paper, triclustering using evolutionary algorithm is implemented using a new fitness function consisting of 3D Mean Square residue (MSR) and Least Square approximation (LSL).
Journal ArticleDOI
Discover gene specific local co-regulations from time-course gene expression data
Ji Zhang,Qigang Gao,Hai Wang +2 more
TL;DR: Experimental results with a real-life gene expression data demonstrate the efficiency and effectiveness of the proposed method for finding gene specific co-regulations using genetic algorithm GA.
Posted ContentDOI
Error modelled gene expression analysis (emogea) provides a superior overview of time course rna-seq measurements and low count gene expression
TL;DR: Error Modelled Gene Expression Analysis (EMOGEA) is presented, a principled framework for analyzing RNA-seq data that incorporates measurement uncertainty in the analysis, while introducing a special formulation for modelling data that are acquired as a function of time or other continuous variable.
References
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