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Background Alternative mRNA splicing is critical to proteomic diversity and tissue and species differentiation. Exclusion of cassette exons, also called exon skipping, is the most common type of alternative splicing in mammals. Results We present a computational model that predicts absolute (though not tissue-differential) percent-spliced-in of cassette exons more accurately than previous models,...
We introduce a generative model of dense flow fields within a layered representation of 3-dimensional scenes. Using probabilistic inference and learning techniques (namely, variational methods), we solve the inverse problem and locally segment the foreground from the background, estimate the nonuniform motion of each, and fill in the disocclusions. To illustrate the usefulness of both the representation...
Image patches can be factorized into ‘shapelets’ that describe segmentation patterns called structural elements (stels), and palettes that describe how to paint the shapelets. We introduce local palettes for patches, global palettes for entire images and universal palettes for image collections. Using a learned shapelet library, patches from a test image can be analyzed using a variational technique...
Subspace segmentation is the task of segmenting data lying on multiple linear subspaces. Its applications in computer vision include motion segmentation in video, structure-from-motion, and image clustering. In this work, we describe a novel approach for subspace segmentation that uses probabilistic inference via a message-passing algorithm. We cast the subspace segmentation problem as that of choosing...
Organs and organelles represent core biological systems in mammals, but the diversity in protein composition remains unclear. Here, we combine subcellular fractionation with exhaustive tandem mass spectrometry-based shotgun sequencing to examine the protein content of four major organellar compartments (cytosol, membranes [microsomes], mitochondria, and nuclei) in six organs (brain, heart, kidney,...
One approach to achieving noise and distortion robust speech recognition is to remove noise and distortion with algorithms of low complexity prior to the use of much higher complexity speech recognizers. This approach has been referred to as cleaning. In this paper we present an approach for speech cleaning using a time-varying, non-linear probabilistic model of a signals log Mel-filter-bank representation...
In this paper, we discuss the use of hierarchical tree-structured Bayesian networks for integrating knowledge concerning contextual relationships between environmentally relevant features extracted from digital imagery at multiple resolution scales. In our model, conditional probability distributions over continuous valued observations are parameterized using a mixture of multivariate Gaussian distributions...
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