Christopher Osgood

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Electrical models for biological cells predict that reducing the duration of applied electrical pulses to values below the charging time of the outer cell membrane (which is on the order of 100 ns for mammalian cells) causes a strong increase in the probability of electric field interactions with intracellular structures due to displacement currents. For(More)
We describe an approach to clustering the yeast protein-protein interaction network in order to identify functional modules, groups of proteins forming multi-protein complexes accomplishing various functions in the cell. We have developed a clustering method that accounts for the small-world nature of the network. The algorithm makes use of the concept of(More)
Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate(More)
We consider the co-clustering of time-varying data using evolutionary co-clustering methods. Existing approaches are based on the spectral learning framework, thus lacking a probabilistic interpretation. We overcome this limitation by developing a probabilistic model in this paper. The proposed model assumes that the observed data are generated via a(More)
BACKGROUND We have initiated an effort to exhaustively map interactions between HTLV-1 Tax and host cellular proteins. The resulting Tax interactome will have significant utility toward defining new and understanding known activities of this important viral protein. In addition, the completion of a full Tax interactome will also help shed light upon the(More)
We describe a novel algorithm for identifying the modular structure of a protein interaction network by computing overlapping clusters. The network is initially decomposed into a high degree network and a residual subnetwork, and clusters are computed separately in both networks, before highly interconnected clusters in both networks are merged. We propose(More)
Deep convolutional neural networks for multi-modality isointense infant brain image segmenta-Deep convolutional neural networks for annotating gene expression patterns in the mouse brain. Automated identification of cell-type-specific genes in the mouse brain by image computing of expression patterns. A mesh generation and machine learning framework for(More)
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