The Predictive Medicine and Data Analytics Lab pursues research in data science. That means we are developing and applying high-dimensional machine learning and statistics methods that can be used for the interrogation of data.

Examples for data we are analyzing are high-throughput data from genomic experiments. For instance, the data can come from gene expression, methylation, copy number variation, DNA-seq or RNA-seq experiments, e.g., generated from next-generation sequencing technologies. In this context, we are interested in basic biological questions relating to E.coli, S.cerevisiae and Human, but our particular focus is on the understanding of biomedical and clinical questions of complex disorders. We have experience working with data from various cancer types, e.g., breast cancer, colon cancer, bladder cancer, prostate cancer, kidney cancer and lymphoma but we are also involved in studies of cystic fibrosis, asthma and diabetes. In addition, we are working on problems studying the pluripotency of Human stem cells and regulatory mechanisms of memory T-Cells in mice. All of these studies are conducted in close collaboration with biologists and clinicians.

Other types of data we are analyzing come from the stock market and social media. We are particularly interested in studying ways to forecast or predict important system parameters, like stock values or consumer behavior, and utilize for this network-based approaches in combination with a statistical framework.



Our general research interest is in data science within the following fields:
• Computational Biology
• Network Science
• Digital Business
• Computational Social Science


Our publication statistics (see also google scholar):
• Journal Articles: 125
• Conference Papers: 29
• Book Chapters: 14
• Books: 16


Our team consists of:
• PhD students: 2
• Research associates: 4


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© Frank Emmert-Streib