Ontology driven integration platform for clinical and translational research

Semantic Web technologies offer a promising framework for integration of disparate biomedical data. In this paper we present the semantic information integration platform under development at the Center for Clinical and Translational Sciences (CCTS) at the University of Texas Health Science Center at Houston (UTHSC-H) as part of our Clinical and Translational Science Award (CTSA) program. We utilize the Semantic Web technologies not only for integrating, repurposing and classification of multi-source clinical data, but also to construct a distributed environment for information sharing, and collaboration online. Service Oriented Architecture (SOA) is used to modularize and distribute reusable services in a dynamic and distributed environment. Components of the semantic solution and its overall architecture are described.

Evaluation of the accuracy of multilevel analysis parameter estimates for intra-cluster correlation in correlated binary data

Many studies in biostatistics deal with binary data. Some of these studies involve correlated observations, which can complicate the analysis of the resulting data. Studies of this kind typically arise when a high degree of commonality exists between test subjects. If there exists a natural hierarchy in the data, multilevel analysis is an appropriate tool for the analysis. Two examples are the measurements on identical twins, or the study of symmetrical organs or appendages such as in the case of ophthalmic studies. Although this type of matching appears ideal for the purposes of comparison, analysis of the resulting data while ignoring the effect of intra-cluster correlation has been shown to produce biased results.^ This paper will explore the use of multilevel modeling of simulated binary data with predetermined levels of correlation. Data will be generated using the Beta-Binomial method with varying degrees of correlation between the lower level observations. The data will be analyzed using the multilevel software package MlwiN (Woodhouse, et al, 1995). Comparisons between the specified intra-cluster correlation of these data and the estimated correlations, using multilevel analysis, will be used to examine the accuracy of this technique in analyzing this type of data. ^

A LAPLACE TRANSFORM PAIR MODEL TO DETERMINE BREMSSTRAHLUNG SPECTRA FROM ATTENUATION DATA (RADIATION, X-RAY, SPECTRAL MODELING)

Every x-ray attenuation curve inherently contains all the information necessary to extract the complete energy spectrum of a beam. To date, attempts to obtain accurate spectral information from attenuation data have been inadequate.^ This investigation presents a mathematical pair model, grounded in physical reality by the Laplace Transformation, to describe the attenuation of a photon beam and the corresponding bremsstrahlung spectral distribution. In addition the Laplace model has been mathematically extended to include characteristic radiation in a physically meaningful way. A method to determine the fraction of characteristic radiation in any diagnostic x-ray beam was introduced for use with the extended model.^ This work has examined the reconstructive capability of the Laplace pair model for a photon beam range of from 50 kVp to 25 MV, using both theoretical and experimental methods.^ In the diagnostic region, excellent agreement between a wide variety of experimental spectra and those reconstructed with the Laplace model was obtained when the atomic composition of the attenuators was accurately known. The model successfully reproduced a 2 MV spectrum but demonstrated difficulty in accurately reconstructing orthovoltage and 6 MV spectra. The 25 MV spectrum was successfully reconstructed although poor agreement with the spectrum obtained by Levy was found.^ The analysis of errors, performed with diagnostic energy data, demonstrated the relative insensitivity of the model to typical experimental errors and confirmed that the model can be successfully used to theoretically derive accurate spectral information from experimental attenuation data. ^

Use of pulsed-field gel electrophoresis in surveillance of Salmonella in Houston, Texas

Background. Pulsed-field gel electrophoresis (PFGE) is a laboratory technique in which Salmonella DNA banding patterns are used as molecular fingerprints for epidemiologic study for "PFGE clusters". State and national health departments (CDC) use PFGE to detect clusters of related cases and to discover common sources of bacteria in outbreaks. ^ Objectives. Using Houston Department of Health and Human Services (HDHHS) data, the study sought: (1) to describe the epidemiology of Salmonella in Houston, with PFGE subtype as a variable; and (2) to determine whether PFGE patterns and clusters detected in Houston were local appearances of PFGE patterns or clusters that occurred statewide. ^ Methods. During the years 2002 to 2005, the HDHHS collected and analyzed data from routine surveillance of Salmonella. We implemented a protocol, between May 1, 2007 and December 31, 2007, in which PFGE patterns from local cases were sent via e-mail to the Texas Department of State Health Services, to verify whether the local PFGE patterns were also part of statewide clusters. PFGE was performed from 106 patients providing a sample from which Salmonella was isolated in that time period. Local PFGE clusters were investigated, with the enhanced picture obtained by linking local PFGE patterns to PFGE patterns at the state and national level. ^ Results. We found that, during the years 2002 to 2005, there were 66 PFGE clusters, ranging in size from 2 to 22 patients within each cluster. Between different serotypes, there were marked differences in the sizes of PFGE clusters. A common source or risk factor was found in fewer than 5 of the 66 PFGE clusters. With the revised protocol, we found that 19 of 66 local PFGE patterns were indistinguishable from PFGE patterns at Texas DSHS. During the eight months, we identified ten local PFGE clusters with a total of 42 patients. The PFGE pattern for eight of the ten clusters matched the PFGE patterns for cases reported to Texas DSHS from other geographic areas. Five of the ten PFGE patterns matched PFGE patterns for clusters under investigation at PulseNet at the national level. HDHHS epidemiologists identified a mode of transmission in two of the ten local clusters and a common risk factor in a third local cluster. ^ Conclusion. In the extended-study protocol, Houston PFGE patterns were linked to patterns seen at the state and national level. The investigation of PFGE clusters was more efficacious in detecting a common transmission when local data were linked to state and national data. ^

Comparison of multiple comparison methods for identifying differential gene expression in simulated and real papillary thyroid cancer microarray data

The difficulty of detecting differential gene expression in microarray data has existed for many years. Several correction procedures try to avoid the family-wise error rate in multiple comparison process, including the Bonferroni and Sidak single-step p-value adjustments, Holm's step-down correction method, and Benjamini and Hochberg's false discovery rate (FDR) correction procedure. Each multiple comparison technique has its advantages and weaknesses. We studied each multiple comparison method through numerical studies (simulations) and applied the methods to the real exploratory DNA microarray data, which detect of molecular signatures in papillary thyroid cancer (PTC) patients. According to our results of simulation studies, Benjamini and Hochberg step-up FDR controlling procedure is the best process among these multiple comparison methods and we discovered 1277 potential biomarkers among 54675 probe sets after applying the Benjamini and Hochberg's method to PTC microarray data.^