Henry Brzeski

Biomedical informatics: development of a comprehensive data warehouse for clinical and genomic breast cancer research.

The Windber Research Institute is an integrated high-throughput research center employing clinical, genomic and proteomic platforms to produce terabyte levels of data. We use biomedical informatics technologies to integrate all of these operations. This report includes information on a multi-year, multi-phase hybrid data warehouse project currently under development in the Institute. The purpose of the warehouse is to host the terabyte-level of internal experimentally generated data as well as data from public sources. We have previously reported on the phase I development, which integrated limited internal data sources and selected public databases. Currently, we are completing phase II development, which integrates our internal automated data sources and develops visualization tools to query across these data types. This paper summarizes our clinical and experimental operations, the data warehouse development, and the challenges we have faced. In phase III we plan to federate additional manual internal and public data sources and then to develop and adapt more data analysis and mining tools. We expect that the final implementation of the data warehouse will greatly facilitate biomedical informatics research.

Functional relationship and gene ontology classification of breast cancer biomarkers

Breast cancer is a complex disease that still imposes a significant healthcare burden on women worldwide. The etiology of breast cancer is not known but significant advances have been made in the area of early detection and treatment. The advent of advanced molecular biology techniques, mapping of the human genome and availability of high throughput genomic and proteomic strategies opens up new opportunities and will potentially lead to the discovery of novel biomarkers for early detection and prognostication of breast cancer. Currently, many biomarkers, particularly the hormonal and epidermal growth factor receptors, are being utilized for breast cancer prognosis. Unfortunately, none of the biomarkers in use have sufficient diagnostic, prognostic and/or predictive power across all categories and stages of breast cancer. It is recognized that more useful information can be generated if tumors are interrogated with multiple markers. But choosing the right combination of biomarkers is challenging, because 1) multiple pathways are involved, 2) up to 62 genes and their protein products are potentially involved in breast cancer-related mechanisms and 3) the more markers evaluated, the more the time and cost involved. This review summarizes the current literature on selected biomarkers for breast cancer, discusses the functional relationships, and groups the selected genes based on a Gene Ontology classification.

An Introduction to Bioinformatics

Bioinformatics is an interdisciplinary field in which new biological insights are discovered from biological data. In this chapter, we first introduce the concept of bioinformatics. Then, we present some typical bioinformatics problems according to the type of biological data. Finally, we provide some challenges in biological data mining and give a short summary at the end of the chapter.