BioDM 2012 - International Workshop on Biological Data Mining and its Applications in Healthcare

Biologists are stepping up their efforts in understanding the biological processes that underlie disease pathways in the clinical contexts. This has resulted in a flood of biological and clinical data from genomic sequences, DNA microarrays, and protein interactions, to biomedical images, disease pathways, and electronic health records. We are in a scenario where our capability to generate biomedical data has greatly surpassed our abilities to mine and analyze the data.

To exploit these biomedical data for discovering new knowledge that can be translated into clinical applications, there are fundamental data analysis difficulties that have to be overcome. Practical issues such as handling noisy and incomplete data (e.g. protein interactions have high false positive and false negative rates), processing compute-intensive tasks (e.g. large scale graph mining), and integrating heterogeneous data sources (e.g. linking genomic data, proteomics data with clinical databases) are new challenges faced by biologists in the post-genome era.

We can expect data mining to play an increasingly crucial role in furthering biological research, since data mining is designed to handle challenging data analysis problems. In fact, it is our hope that data mining will be the next technical innovation employed by biologists to enable them to make insightful observations and groundbreaking discoveries from their wide array of heterogeneous data from molecular biology to pharmaceutical and clinical domains. .

There are therefore unprecedented opportunities for data mining researchers from the computer science domain to contribute to this meaningful scientific pursuit together with the biologists and clinical scientists. The mission of this workshop is to disseminate the research results and best practices of data mining approaches to the cross-disciplinary researchers and practitioners from both the data mining disciplines and the life sciences domains. We therefore encourage submission of papers using data mining techniques to address the challenging issues in various biological data analysis. In particular, we especially welcome the submissions reporting data mining techniques in healthcare related applications that integrate the use of biological data in a clinical context for translational research.