@article {49744,
	title = {Accurate proteome-wide protein quantification from high-resolution 15N mass spectra.},
	journal = {Genome Biol},
	volume = {12},
	year = {2011},
	month = {2011},
	pages = {R122},
	abstract = {<p>In quantitative mass spectrometry-based proteomics, the metabolic incorporation of a single source of 15N-labeled nitrogen has many advantages over using stable isotope-labeled amino acids. However, the lack of a robust computational framework for analyzing the resulting spectra has impeded wide use of this approach. We have addressed this challenge by introducing a new computational methodology for analyzing 15N spectra in which quantification is integrated with identification. Application of this method to an Escherichia coli growth transition reveals significant improvement in quantification accuracy over previous methods.</p>},
	keywords = {algorithms, Amino Acid Sequence, Bacterial Proteins, Escherichia coli, Isotope Labeling, Mass Spectrometry, Molecular Sequence Data, Nitrogen Isotopes, Proteome, proteomics, Sensitivity and Specificity, software},
	issn = {1474-760X},
	doi = {10.1186/gb-2011-12-12-r122},
	author = {Khan, Zia and Amini, Sasan and Bloom, Joshua S and Ruse, Cristian and Caudy, Amy A and Kruglyak, Leonid and Singh, Mona and Perlman, David H and Tavazoie, Saeed}
}
@proceedings {38343,
	title = {Inexact Local Alignment Search over Suffix Arrays},
	year = {2009},
	month = {2009},
	publisher = {IEEE},
	type = {10.1109/BIBM.2009.25},
	abstract = {We describe an algorithm for finding approximate seeds for DNA homology searches. In contrast to previous algorithms that use exact or spaced seeds, our approximate seeds may contain insertions and deletions. We present a generalized heuristic for finding such seeds efficiently and prove that the heuristic does not affect sensitivity. We show how to adapt this algorithm to work over the memory efficient suffix array with provably minimal overhead in running time. We demonstrate the effectiveness of our algorithm on two tasks: whole genome alignment of bacteria and alignment of the DNA sequences of 177 genes that are orthologous in human and mouse. We show our algorithm achieves better sensitivity and uses less memory than other commonly used local alignment tools.},
	keywords = {bacteria, Bioinformatics, biology computing, Computational Biology, Costs, DNA, DNA homology searches, DNA sequences, Educational institutions, generalized heuristic, genes, Genetics, genome alignment, Genomics, human, inexact local alignment search, inexact seeds, local alignment, local alignment tools, memory efficient suffix array, microorganisms, molecular biophysics, mouse, Organisms, Sensitivity and Specificity, sequences, suffix array, USA Councils},
	isbn = {978-0-7695-3885-3},
	author = {Ghodsi, M. and M. Pop}
}
@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 = {<p>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.</p>},
	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}
}
