BIOKDD 2016 - 7 th International Workshop on Biological Knowledge Discovery and Data Mining

With the development of Molecular Biology during the last decades, we are witnessing an exponential growth of both the volume and the complexity of biological data. For example, the Human Genome Project provided the sequence of the 3 billion DNA bases that constitute the human genome. And,
consequently, we are provided too with the sequences of about 100,000 proteins. Therefore, we are entering the post-genomic era: after having focused so many efforts on the accumulation of data, we have now to focus as much effort, and even more, on the analysis of these data. Analyzing this huge volume of
data is a challenging task because, not only, of its complexity and its multiple and numerous correlated factors, but also, because of the continuous evolution of our understanding of the biological mechanisms. Classical approaches of biological data analysis are no longer efficient and produce only a very limited
amount of information, compared to the numerous and complex biological mechanisms under study. From here comes the necessity to use computer tools and develop new in silico high performance approaches to support us in the analysis of biological data and, hence, to help us in our understanding of
the correlations that exist between, on one hand, structures and functional patterns of biological sequences and, on the other hand, genetic and biochemical mechanisms. Knowledge Discovery and Data Mining (KDD) are a response to these new trends.
Topics of BIOKDD workshop include, but not limited to:
Data Preprocessing: Biological Data Storage, Representation and Management (data warehouses, databases, sequences, trees, graphs, biological networks and pathways, …), Biological Data Cleaning (errors removal, redundant data removal, completion of missing data, …), Feature Extraction
(motifs, subgraphs, …), Feature Selection (filter approaches, wrapper approaches, hybrid approaches, embedded approaches, …)
Data Mining: Biological Data Regression (regression of biological sequences…), Biological data clustering/biclustering (microarray data biclustering, clustering/biclustering of biological sequences, …), Biological Data Classification (classification of biological sequences…), Association Rules Learning from Biological Data, Text mining and Application to Biological Sequences, Web mining and Application to Biological Data, Parallel, Cloud and Grid Computing for Biological Data Mining
Data Postprocessing: Biological Nuggets of Knowledge Filtering, Biological Nuggets of Knowledge Representation and Visualization, Biological Nuggets of Knowledge Evaluation (calculation of the classification error rate, evaluation of the association rules via numerical indicators, e.g. measurements of interest, … ), Biological Nuggets of Knowledge Integration