Toward a systems biology software toolkit

Abstract

Insight to complex problems may be revealed when domain data sets are viewed or structured in new and innovative ways. Systems approaches to biomedical problems fundamentally involve forecasting, and a deliberate integration of diverse data sources. Adding a clinical proteomic dimension to these developing efforts affirms the ultimate aims of enhanced diagnostic prediction, maximizing the possibility for a rational therapy individualized to a patient's pathologic process, and thus, improving patient outcomes. Consistent with these evolving goals, computer-aided diagnostic systems are rapidly approaching a new paradigm involving the integration of medical imaging with high throughput molecular medicine tests. As medical imaging systems become more sensitive in finding anatomic anomalies, their lack of specificity becomes much more of a clinical dilemma. Proteomic tests may be used to help resolve the lack of specificity of imaging findings. A synergistic test composed of a targeted imaging study correlated with a genomic or proteomic test(s), offers the potential of a tremendous medical advancement. Bioinformatic software toolkits are crucial components of these systems. Open source software provides a mechanism for leveraging existing toolkits, sharing expertise, accelerating development, and furthering biomedical, software, and systems sciences in new and complex multi-disciplinary fields. Our evolving toolkit utilizes components from several existing open source projects. It will initially be customized for serum proteomic pattern diagnostics, which will be used in the upcoming NCI/CCR Clinical Trial involving the monitoring for ovarian cancer recurrence.

Discovering Clinical Biomarkers of Ionizing Radiation Exposure with Serum Proteomic Analysis

“…1A. Given the heterogeneity present in the data due to large variety of diseases/cancers (18), as well as a wide range of both anatomic sites (9) and degree of radiation received, the visual data mining activities aided greatly in the identification of interesting ion patterns for additional pursuit (25). Custom T-SQL stored procedures were written to further aid in feature selection through the implementation of a minimax algorithmic procedure to maximize the differences between candidate ion groups while minimizing their coefficient of variation.…”