Robert F. Murphy Professor of Biological Sciences Professor of Biomedical Engineering Professor, Machine Learning Department Director, Ray and Stephanie Lane Center for Computational Biology Ph.D., California Institute of Technology Postdoctoral Appointment, Columbia University murphy@cmu.edu Home Page 412-268-3480 (Phone) 412-268-7129 (Fax) 7723 Gates/Hillman Center Department of Biological Sciences Carnegie Mellon University Pittsburgh, PA 15213

My laboratory's work combines my interests in cell and computational biology. Over the past ten years, the increased availability of sophisticated light microscope imaging systems has led to an explosion in the acquisition of digital images by biologists. Combined with the development of dozens of new fluorescent probes, advances in instrumentation have enabled cell biologists to address questions not contemplated a decade ago. This has created a largely unmet need for sophisticated software systems to analyze the resulting digital microscope images. We have therefore developed tools for objectively choosing a representative image from a set, tools for comparing sets of images, systems for automating the determination of subcellular location, and tools for organizing unknown proteins by their location patterns. The critical component of each of the systems is a set of numerical features that capture essential biological information in the images. The work has implications for automated characterization of newly identified proteins and for high-throughput drug screening using microscopy.

Selected Publications

Chen SC and Murphy RF. A graphical model approach to automated classification of protein subcellular location patterns in multi-cell images. BMC Bioinformatics, 7:90, 2006.

Murphy RF, Velliste M and Porreca G. Robust numerical features for description and classification of subcellular location patterns in fluorescence microscope images. J. VLSI Sig. Proc, 35:311-321, 2003.

Huang K, Velliste M and Murphy RF. Feature reduction for improved recognition of subcellular location patterns in fluorescence microscope images. Proc. SPIE, 4962:307-318, 2003.

Chen X, Velliste M, Weinstein S, Jarvik JW and Murphy RF. Location proteomics - Building subcellular location trees from high resolution 3D fluorescence microscope images of randomly-tagged proteins. Proc. SPIE, 4962:298-306, 2003.

Murphy RF, Velliste M and Porreca G. Robust classification of subcellular location patterns in fluorescence microscope images. Proceedings of the 2002 IEEE International Workshop on Neural Networks for Signal Processing (NNSP 12), 67-76, 2002.