@article {49679, title = {SplicePort--an interactive splice-site analysis tool.}, journal = {Nucleic Acids Res}, volume = {35}, year = {2007}, month = {2007 Jul}, pages = {W285-91}, abstract = {

SplicePort is a web-based tool for splice-site analysis that allows the user to make splice-site predictions for submitted sequences. In addition, the user can also browse the rich catalog of features that underlies these predictions, and which we have found capable of providing high classification accuracy on human splice sites. Feature selection is optimized for human splice sites, but the selected features are likely to be predictive for other mammals as well. With our interactive feature browsing and visualization tool, the user can view and explore subsets of features used in splice-site prediction (either the features that account for the classification of a specific input sequence or the complete collection of features). Selected feature sets can be searched, ranked or displayed easily. The user can group features into clusters and frequency plot WebLogos can be generated for each cluster. The user can browse the identified clusters and their contributing elements, looking for new interesting signals, or can validate previously observed signals. The SplicePort web server can be accessed at http://www.cs.umd.edu/projects/SplicePort and http://www.spliceport.org.

}, keywords = {Base Sequence, Chromosome mapping, Computational Biology, Computer simulation, DNA, Genome, HUMANS, Internet, Models, Genetic, Molecular Sequence Data, Pattern Recognition, Automated, RNA Splice Sites, sequence alignment, Sequence Analysis, DNA, User-Computer Interface}, issn = {1362-4962}, doi = {10.1093/nar/gkm407}, author = {Dogan, Rezarta Islamaj and Getoor, Lise and Wilbur, W John and Mount, Stephen M} } @article {49560, title = {MCMC data association and sparse factorization updating for real time multitarget tracking with merged and multiple measurements.}, volume = {28}, year = {2006}, month = {2006 Dec}, pages = {1960-72}, abstract = {

In several multitarget tracking applications, a target may return more than one measurement per target and interacting targets may return multiple merged measurements between targets. Existing algorithms for tracking and data association, initially applied to radar tracking, do not adequately address these types of measurements. Here, we introduce a probabilistic model for interacting targets that addresses both types of measurements simultaneously. We provide an algorithm for approximate inference in this model using a Markov chain Monte Carlo (MCMC)-based auxiliary variable particle filter. We Rao-Blackwellize the Markov chain to eliminate sampling over the continuous state space of the targets. A major contribution of this work is the use of sparse least squares updating and downdating techniques, which significantly reduce the computational cost per iteration of the Markov chain. Also, when combined with a simple heuristic, they enable the algorithm to correctly focus computation on interacting targets. We include experimental results on a challenging simulation sequence. We test the accuracy of the algorithm using two sensor modalities, video, and laser range data. We also show the algorithm exhibits real time performance on a conventional PC.

}, keywords = {algorithms, Artificial Intelligence, Image Enhancement, Image Interpretation, Computer-Assisted, Information Storage and Retrieval, Movement, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique}, issn = {0162-8828}, doi = {10.1109/TPAMI.2006.247}, author = {Khan, Zia and Balch, Tucker and Dellaert, Frank} } @article {49562, title = {MCMC-based particle filtering for tracking a variable number of interacting targets.}, volume = {27}, year = {2005}, month = {2005 Nov}, pages = {1805-19}, abstract = {

We describe a particle filter that effectively deals with interacting targets--targets that are influenced by the proximity and/or behavior of other targets. The particle filter includes a Markov random field (MRF) motion prior that helps maintain the identity of targets throughout an interaction, significantly reducing tracker failures. We show that this MRF prior can be easily implemented by including an additional interaction factor in the importance weights of the particle filter. However, the computational requirements of the resulting multitarget filter render it unusable for large numbers of targets. Consequently, we replace the traditional importance sampling step in the particle filter with a novel Markov chain Monte Carlo (MCMC) sampling step to obtain a more efficient MCMC-based multitarget filter. We also show how to extend this MCMC-based filter to address a variable number of interacting targets. Finally, we present both qualitative and quantitative experimental results, demonstrating that the resulting particle filters deal efficiently and effectively with complicated target interactions.

}, keywords = {algorithms, Animals, Artificial Intelligence, Computer simulation, HUMANS, Image Enhancement, Image Interpretation, Computer-Assisted, Information Storage and Retrieval, Markov chains, Models, Biological, Models, Statistical, Monte Carlo Method, Motion, Movement, Pattern Recognition, Automated, Subtraction Technique, Video Recording}, issn = {0162-8828}, doi = {10.1109/TPAMI.2005.223}, author = {Khan, Zia and Balch, Tucker and Dellaert, Frank} }