Biomedical subjects

Kristin P Bennett

Publications and source records attributed to Kristin P Bennett.

3 recordsLinked to original sources

PubMed2006

Identifying Mycobacterium tuberculosis complex strain families using spoligotypes.

We present a novel approach for analysis of Mycobacterium tuberculosis complex (MTC) strain genotyping data. Our work presents a first step in an ongoing project dedicated to the development of decision support tools for tuberculosis (TB) epidemiologists exploiting both genotyping and epidemiological data. We focus on spacer oligonucleotide typing (spoligotyping), a genotyping method based on analysis of a direct repeat (DR) locus. We use mixture models to identify strain families of MTC based on their spoligotyping patterns. Our algorithm, SPOTCLUST, incorporates biological information on spoligotype evolution, without attempting to derive the full phylogeny of MTC. We applied our algorithm to 535 different spoligotype patterns identified among 7166 MTC strains isolated between 1996 and 2004 from New York State TB patients. Two models were employed and validated: a 36-component model based on global spoligotype database SpolDB3, and a randomly initialized model (RIM) containing 48 components. Our analysis both confirmed previously expert-defined families of MTC strains and suggested certain new families. SPOTCLUST, which is available online, can be further improved by incorporating data obtained using additional strain genetic markers and epidemiological information. We demonstrate on New York City (NYC) patient data how the resulting models can potentially form the basis of TB control tools using genotyping.

Adolescent↗

PubMed2003

Prediction of the effect of mobile-phase salt type on protein retention and selectivity in anion exchange systems.

This study examines the effect of different salt types on protein retention and selectivity in anion exchange systems. Particularly, linear retention data for various proteins were obtained on two structurally different anion exchange stationary-phase materials in the presence of three salts with different counterions. The data indicated that the effects are, for the most part, nonspecific, although various specific effects could also be observed. Quantitative structure retention relationship (QSRR) models based on support vector machine feature selection and regression models were developed using the experimental chromatographic data in conjunction with various molecular descriptors computed from protein crystal structure geometries. Star plots for each descriptor used in the final model were generated to aid in interpretation. The resulting QSRR models were predictive, with cross-validated r2 values of 0.9445, 0.9676, and 0.8897 for Source 15Q and 0.9561, 0.9876, and 0.9760 for Q Sepharose resins in the presence of three different salts. The predictive power of these models was validated using a set of test proteins that were not used in the generation of these models. Interpretation of the models revealed that particular trends for proteins and salts could be captured using QSRR techniques.

Algorithms↗

PubMedDate not supplied

Prediction of protein retention times in anion-exchange chromatography systems using support vector regression.

Quantitative Structure-Retention Relationship (QSRR) models are developed for the prediction of protein retention times in anion-exchange chromatography systems. Topological, subdivided surface area, and TAE (Transferable Atom Equivalent) electron-density-based descriptors are computed directly for a set of proteins using molecular connectivity patterns and crystal structure geometries. A novel algorithm based on Support Vector Machine (SVM) regression has been employed to obtain predictive QSRR models using a two-step computational strategy. In the first step, a sparse linear SVM was utilized as a feature selection procedure to remove irrelevant or redundant information. Subsequently, the selected features were used to produce an ensemble of nonlinear SVM regression models that were combined using bootstrap aggregation (bagging) techniques, where various combinations of training and validation data sets were selected from the pool of available data. A visualization scheme (star plots) was used to display the relative importance of each selected descriptor in the final set of "bagged" models. Once these predictive models have been validated, they can be used as an automated prediction tool for virtual high-throughput screening (VHTS).

Anion Exchange Resins↗