Data Integration and Knowledge Discovery in Life Sciences

Recent advances in various forms of omics technologies have generated huge amount of data. To fully exploit these data sets that in many cases are publicly available, robust computational methodologies need to be developed to deal with the storage, integration, analysis, visualization, and dissemination of these data. In this paper, we describe some of our research activities in data integration leading to novel knowledge discovery in life sciences. Our multistrategy approach with integration of prior knowledge facilitates a novel means to identify informative genes that could have been missed by the commonly used methods. Our transcriptomics-proteomics integrative framework serves as a means to enhance the confidence of and also to complement transcriptomics discovery. Our new research direction in integrative data analysis of omics data is targeted to identify molecular associations to disease and therapeutic response signatures. The ultimate goal of this research is to facilitate the development of clinical test-kits for early detection, accurate diagnosis/prognosis of disease, and better personalized therapeutic management.