3 Similarity of gene expression time-series 8 3.

High throughput biological data need to be processed, analyzed, and interpreted to address problems in life sciences. Bioinformatics, computational biology, and systems biology deal with biological problems using computational methods. Clustering is one of the methods used to gain insight into biological processes, particularly at the genomics level. Clearly, clustering can be used in many area...

In many applications, the expert interpretation of coclustering is easier than for mono-dimensional clustering. Co-clustering aims at computing a bi-partition that is a collection of co-clusters: each co-cluster is a group of objects associated to a group of attributes and these associations can support interpretations. Many constrained clustering algorithms have been proposed to exploit the do...

Facing the development of microarray technology, clustering is currently a leading technique to gene expression data analysis. In this paper, we propose a novel algorithm called repulsive clustering, which is developed for the use of gene expression data analysis. One common goal to achieve on developing gene expression data clustering algorithms is to acquire a higher quality output. Our perfo...

Analysis of microarray gene expression data is important for disease study at the molecular and genomic level. Computational data modeling and analysis are essential for extracting meaningful and specific information from noisy, high-throughput, and large-scale microarray gene expression data. In this dissertation, we propose and develop innovative data modeling and analysis methods for learnin...