Kristin H. Jarman

Learn More
We evaluate statistical models used in two-hypothesis tests for identifying peptides from tandem mass spectrometry data. The null hypothesis H(0), that a peptide matches a spectrum by chance, requires information on the probability of by-chance matches between peptide fragments and peaks in the spectrum. Likewise, the alternate hypothesis H(A), that the(More)
Automatic de novo peptide identification from collision-induced dissociation tandem mass spectrometry data is made difficult by large plateaus in the fitness landscapes of scoring functions and the fuzzy nature of the constraints that is due to noise in the data. Two different scoring functions are combined into a parallel multi-objective optimization(More)
MOTIVATION Peptide identification following tandem mass spectrometry (MS/MS) is usually achieved by searching for the best match between the mass spectrum of an unidentified peptide and model spectra generated from peptides in a sequence database. This methodology will be successful only if the peptide under investigation belongs to an available database.(More)
Metabonomics involves the quantitation of the dynamic multivariate metabolic response of an organism to a pathological event or genetic modification [J.K. Nicholson, J.C. Lindon, E. Holmes, Xenobiotica 29 (1999) 1181-1189]. The analysis of these data involves the use of appropriate multivariate statistical methods; Principal Component Analysis (PCA) has(More)
In the aftermath of the 2001 anthrax letters, researchers have been exploring ways to predict the production environment of unknown-source microorganisms. Culture medium, presence of agar, culturing temperature, and drying method are just some of the broad spectrum of characteristics an investigator might like to infer. The effects of many of these factors(More)
Peptide identification following tandem mass spectrometry is usually achieved by searching for the best match between the mass spectrum of an unidentified peptide and those available in a database. This methodology will be successful only if the peptide under investigation belongs to an available database. In this paper, we propose a Genetic Algorithm (GA)(More)
— This paper presents a feature selection methodology that can be applied to datasets containing a mixture of continuous and categorical variables. Using a Genetic Algorithm (GA), this method explores a dataset and selects a small set of features relevant for the prediction of a binary (1/0) response. Binary classification trees and an objective function(More)
  • 1