Frequency-based time-series gene expression recomposition using PRIISM

Meteorological Time Series Modeling Using an Adaptive Gene Expression Programming

The precipitations are characterized by important spatial and temporal variation. Model determination for such series is of high importance for hydrological purposes (e.g. weather forecasting, agriculture, flood areas, administrative planning), even if discovering patterns in such series is a very difficult problem. The objective of the current study is to describe the use of an adaptive evolut...

Clustering Time-Series Gene Expression Data Using Smoothing Spline Derivatives

Microarray data acquired during time-course experiments allow the temporal variations in gene expression to be monitored. An original postprandial fasting experiment was conducted in the mouse and the expression of 200 genes was monitored with a dedicated macroarray at 11 time points between 0 and 72 hours of fasting. The aim of this study was to provide a relevant clustering of gene expression...

Frequency-based Analysis of Financial Time Series

Clustering Gene Expression Time Series Data

Efficiently and effectively finding the genes with similar behaviors from microarray data is an important task in bioinformatics community. Co-expression genes have the same behavior or are controlled by the same regulatory mechanisms. Clustering analysis is a very popular technique to group the co-expressed genes into the same cluster. One of the key issues for clustering gene expression time ...

Clustering short time series gene expression data

MOTIVATION Time series expression experiments are used to study a wide range of biological systems. More than 80% of all time series expression datasets are short (8 time points or fewer). These datasets present unique challenges. On account of the large number of genes profiled (often tens of thousands) and the small number of time points many patterns are expected to arise at random. Most clu...