Covariance Matrix Adaptation for Multiobjective Multiarmed Bandits.

Upper confidence bound (UCB) is a successful multiarmed bandit for regret minimization. The covariance matrix adaptation (CMA) for Pareto UCB (CMA-PUCB) algorithm considers stochastic reward vectors with correlated objectives. We upper bound the cumulative pseudoregret of pulling suboptimal arms for the CMA-PUCB algorithm to logarithmic number of arms K , objectives D , and samples n , O (ln(nDK) ∑ (|| Σ ||/∆)) , using a variant of Berstein inequality for matrices, where ∆ is the regret of pulling the suboptimal arm i .

Recombination operators and selection strategies for evolutionary Markov Chain Monte Carlo algorithms.

Markov Chain Monte Carlo (MCMC) methods are often used to sample from intractable target distributions. Some MCMC variants aim to improve the performance by running a population of MCMC chains. In this paper, we investigate the use of techniques from Evolutionary Computation (EC) to design population-based MCMC algorithms that exchange useful information between the individual chains.

Geometrical recombination operators for real-coded evolutionary MCMCs.

Markov chain Monte Carlo (MCMC) algorithms are sampling methods for intractable distributions. In this paper, we propose and investigate algorithms that improve the sampling process from multi-dimensional real-coded spaces. We present MCMC algorithms that run a population of samples and apply recombination operators in order to exchange useful information and preserve commonalities in highly probable individual states.

LysNDeNovo: an algorithm enabling de novo sequencing of Lys-N generated peptides fragmented by electron transfer dissociation.

Recently, we introduced a novel proteomics method employing a metalloendopeptidase with Lys-N specificity to produce proteolytic peptides. Fragmentation spectra generated by electron transfer dissociation, for a large proportion of the Lys-N proteolytic peptides, were found to be dominated by extensive series of c-type ions. Taking advantage of this unique spectral property, we developed an algorithm, LysNDeNovo, to facilitate de novo sequencing of these peptides.

StatQuant: a post-quantification analysis toolbox for improving quantitative mass spectrometry.

Motivation: Mass spectrometric protein quantitation has emerged as a high-throughput tool to yield large amounts of data on peptide and protein abundances. Currently, differential abundance data can be calculated from peptide intensity ratios by several automated quantitation software packages available. There is, however, still a great need for additional processing to validate and refine the quantitation results.

Strong cation exchange-based fractionation of Lys-N-generated peptides facilitates the targeted analysis of post-translational modifications.

In proteomics multi-dimensional fractionation techniques are widely used to reduce the complexity of peptide mixtures subjected to mass spectrometric analysis. Here, we describe the sequential use of strong cation exchange and reversed phase liquid chromatography in the separation of peptides generated by a relatively little explored metallo-endopeptidase with Lys-N cleavage specificity. When such proteolytic peptides are subjected to low-pH strong cation exchange we obtain fractionation profiles in which peptides from different functional categories are well separated.

Straightforward ladder sequencing of peptides using a Lys-N metalloendopeptidase.

We introduce a method for sequencing peptides by mass spectrometry using a metalloendopeptidase that cleaves proteins at the amino side of lysine (Lys-N). When analyzed by electron transfer dissociation (ETD)-based mass spectrometric sequencing, Lys-N-digested peptides that contain a single lysine residue produce spectra dominated by c-type fragment ions, providing simple ladders for sequence determination. This method should be a valuable strategy for de novo sequencing and the analysis of post-translational modifications.