Microarray technology can be used to collect gene expression data in bulk. In order to be able to deal with this large amount of data that can now be produced, an efficient method of computing the relationships of this data must be constructed. Some attempts at applying neural networks have been employed for this task. For this project we intend to implement several neural network architectures...

In the past decade there have been advance in technologies, the amount of biological data such as DNA sequences and microarray data have been increased tremendously. To obtain knowledge from the data, explore relationships between genes, understanding severe diseases and development of drugs for patterns from the databases of large size and high dimensionality. Information retrieval and data mi...

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 ...

MOTIVATION Many clustering algorithms have been proposed for the analysis of gene expression data, but little guidance is available to help choose among them. We provide a systematic framework for assessing the results of clustering algorithms. Clustering algorithms attempt to partition the genes into groups exhibiting similar patterns of variation in expression level. Our methodology is to app...

n data mining, clustering techniques have been applied in cellular processes, gene regulation, sub types of cells and gene function. Clustering in microarray gene expression handles various experimental conditions in various algorithms by using different data sets. This paper focuses the study on the clustering of gene expression data using the data sets such as yeast data, yeast cell-cycle, se...

Recent work has used graphs to modelize expression data from microarray experiments, in view of partitioning the genes into clusters. In this paper, we introduce the use of a decomposition by clique separators. Our aim is to improve the classical clustering methods in two ways: first we want to allow an overlap between clusters, as this seems biologically sound, and second we want to be guided ...

Microarray experiments have been used to measure genes’ expression levels under different cellular conditions or along certain time course. Initial attempts to interpret these data begin with grouping genes according to similarity in their expression profiles. The widely adopted clustering techniques for gene expression data include hierarchical clustering, self-organizing maps, and K-means clu...

Microarray gene expressions provide an insight into genomic biomarkers that aid in identifying cancerous cells and normal cells. In this study, functionally related genes are identified by partitioning gene data. Clustering is an unsupervised learning technique that partition gene data into groups based on the similarity between their expression profiles. This identifies functionally related ge...

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...