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List of meetings :

  • Elias Egho - A FCA-based analysis of sequential care trajectories
13 October 2011 13:00:00, B013
This paper presents a research work in the domains of sequential pattern mining and formal concept analysis. Using a combined method, we show how concept lattices and interestingness measures such as stability can improve the task of discovering knowledge in symbolic sequential data. We give example of a real medical application to illustrate how this approach can be useful to discover patterns of trajectories of care in a french medico-economical database.

  • Emmanuel Bresso - Use of domain knowledge for dimension reduction: application to mining of drug side effects
25 October 2011 13:00:00, B013
High dimensionality of datasets can impair the execution of most data mining programs and lead to the production of numerous and complex patterns, inappropriate for interpretation by the experts. Thus, dimension reduction of datasets constitutes an important research orientation in which the role of domain knowledge is essential.
We present here a new approach for reducing dimensions in a dataset by exploiting semantic relationships between terms of an ontology structured as a rooted directed acyclic graph. Term clustering is performed thanks to the recently described IntelliGO similarity measure and the term clusters are then used as descriptors for data representation. The strategy reported here is applied to a set of drugs associated with their side effects collected from the SIDER database. Terms describing side effects belong to the MedDRA terminology. The hierarchical clustering of about 1,200 MedDRA terms into an optimal collection of 112 term clusters leads to a reduced data representation. Two data mining experiments are then conducted to illustrate the advantage of using this reduced representation. Practise for KDIR presentation, 3rd International Conference on Knowledge Discovery and Information Retrieval, 26-28 octobre 2011, Paris.

  • Marie-Dominique Devignes - Ontology-based functional classification of genes : evaluation with reference sets and overlap analysis
9 November 2011 14:00:00, B013
Functional classification involves grouping genes according to their molecular functions or the biological processes they participate in. This unsupervised classification task is essential for interpreting gene datasets produced by post-genomic experiments. As the functional annotation of genes is mostly based on the Gene Ontology (GO), many similarity measures using the GO have been described, but few of them have been used for clustering.
In this paper we evaluate functional classification of genes using the IntelliGO semantic similarity measure with the help of reference sets. These sets consist of genes taken from human and yeast KEGG pathways and Pfam clans. Hierarchical clustering and heatmap visualization are used to illustrate the advantages of IntelliGO over several other measures. Because genes often belong to more than one reference set, the fuzzy C-means clustering algorithm is then applied to the datasets using IntelliGO. The F-score method is used to estimate the quality of clustering and the optimal number of clusters. The results are compared with those obtained from the state-of-the-art DAVID functional classification method. Overlap analysis allows to study the matching between clusters and reference sets, and leads us to propose a set-difference method for discovering missing information. The IntelliGO similarity measure, the clustering tool and the reference sets used for evaluation are available at:

  • Florence D'Alché-Buc - bientôt
16 June 2013 00:00:00, bientôt