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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Journal ArticleDOI
Finding Significantly Expressed genes from time-course expression profiles
TL;DR: Results show that the proposed method outperforms traditional methods for finding Significantly Expressed (SE) genes from time-course expression.
Temporal reasoning in medicine using dynamic Bayesian networks
TL;DR: This dissertation presents various DBN models and data preparation techniques that detect sepsis with very high accuracy within two hours after the patients’ admission to the emergency department, and recommends methods and techniques for temporal reasoning in medicine.
Journal ArticleDOI
Aplicação da análise de agrupamento de dados de expressão gênica temporal a dados em painel
TL;DR: In this paper, a melhor alternativa, entre os metodos de agrupamento hierarquico (Ward) and de otimizacao (Tocher), for a formacao de grupos homogeneos de series de expressao genica, and realizar previsoes quanto a expressa genica dessas series, a partir de pequeno numero de observacoes temporais.
Proceedings ArticleDOI
A novel fuzzy and multiobjective evolutionary algorithm based gene assignment for clustering short time series expression data
TL;DR: The proposed template based clustering algorithm has shown better or similar performance compared to STEM and better than k-means on a real biological data and was able to distinguish between real and noisy pattern when tested on artificial and real biologicalData.
References
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