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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Proceedings ArticleDOI
Revisiting the yeast cell cycle problem with the improved TriGen algorithm
TL;DR: Improvements on the TriGen genetic algorithm are presented and the results of applying the improved TriGen algorithm to the yeast cell cycle problem are presented, where the goal is to identify all genes whose expression levels are regulated by the cell cycle.
Journal ArticleDOI
Multiconstrained gene clustering based on generalized projections
TL;DR: The POCS-based MGC method can successfully combine multiple constraints from different nature for gene clustering and the proposed GLL is an effective performance measure for the soft clustering solutions.
Posted ContentDOI
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, i.e. for each case-control pair individually, using Gaussian processes in order to identify differentially expressed genes (DEGs) and combines the lists of DEGs on a pathway-level using a permutation-based empirical hypothesis testing to overcome gene-level variability and inconsistencies prevalent to heterogeneous datasets from complex diseases.
Dissertation
A systems biology approach to signalling pathway and gene network regulation modelling in Mastitis
van Loenen-den Breems,Y Nicoline +1 more
TL;DR: It is suggested that the manuscript should be rewritten in a chapters-by- chapters format to facilitate more detailed discussion of the author's research and its findings.
References
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