Paola Salle

Mining Discriminant Sequential Patterns for Aging Brain

Discovering new information about groups of genes implied in a disease is still challenging. Microarrays are a powerful tool to analyse gene expression. In this paper, we propose a new approach outlining relationships between genes based on their ordered expressions. Our contribution is twofold. First, we propose to use a new material, called sequential patterns, to be investigated by biologists. Secondly, due to the expression matrice density, extracting sequential patterns from microarray datasets is far away from being easy. The aim of our proposal is to provide the biological experts with an efficient approach based on discriminant sequential patterns. Results of various experiments on real biological data highlight the relevance of our proposal.

Using OWA operators for gene sequential pattern clustering

Nowadays, the management of sequential patterns data becomes an increasing need in biological knowledge discovery processes. An important task in these processes is the restitution of the results obtained by using data mining methods. In a complex domain as biomedical, an efficient interpretation of the patterns without any assistance is difficult. One of the most common knowledge discovery proces is clustering. But the application of clustering to gene sequential patterns is far from easy on biomedical data. In this paper, we introduce a new gene sequential patterns similarity function and summarization algorithm.

Handling fuzzy gaps in sequential patterns: Application to health

Dealing with digital data for mining novel knowledge is a non trivial task that has received much attention in the last years. However, it is still not easy to handle such data, especially when large volumes of values must be analyzed. In our work, we focus on biological data from DNA chips that biologists study in order to try and discover new gene correlations that could help understanding diseases like breast cancer. In this framework, we consider the values from the DNA microarrays, which convey the behavior of some genes, and we want to discover how these behaviors are correlated. This data are digital values that can be ordered and sorted. In previous work, sequential patterns like {(1 5)(2)} have been discovered, meaning that genes 1 and 5 have the same expression level followed by gene 2 that has a higher expression value. However, such data are very noisy and considering close values as ordered is often false. We thus consider here fuzzy rankings based on a fuzzy partition provided by the experts. Rules can then better characterize how genes are correlated.