The biologists are stepping up their efforts to understand the biological processes that underlie disease pathways. 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 situation where our ability to generate biomedical data has greatly surpassed our ability to mine and analyze the data. 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 still many fundamental data analysis challenges to be overcome in order to discover new knowledge from the biomedical data to translate into clinical applications. These include 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). This is an unprecedented opportunity for data mining researchers from the computer science domain to contribute to the meaningful scientific pursuit together with the biologists and clinical scientists. This workshop’s mission 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 encourage submission of papers describing the design and use of data mining techniques to address the various challenging issues in biological data analysis. We particularly welcome paper submissions that report the development of data mining techniques in healthcare-related applications that integrate the use of biological data in a clinical context for translational research.