Towards integrative data analysis for predictive modeling in biomedical computing

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The proposed research is aimed at developing innovative methods to support our understanding of complex biological phenomena. The new era of large-scale biology is characterized by insurmountable amounts of data including high throughput genetic data, large public databases, medical images and clinical signals. Data abundance stands in contrast to the limited knowledge about the biological events underlying disease. The proposed research seeks to close this gap through the development of feature extraction methods that exploit multi-modal biomedical data for informative attributes, and modeling approaches that integrate these attributes to explain disease processes. The computational methods sought will advance the state of the art in machine learning, image and signal processing, and software engineering.