Introduction: Knowledge Discovery in High-Throughput Biological Domains

Abstract

The sequencing of the human genome was a major step in understanding the ways in which we are wired. Although an important milestone, this genetic blueprint provides only a ``parts list"; it does not offer any information about how the human organism is actually working, and it gives little insight into the function or interactions among the approximately thirty thousand constitutive parts that comprise our genome. To date, research in molecular biology had resulted in annotating only a small percentage (around 10%) of the gene set, and even less is known about proteins. Because of the quantity of information being generated, we increasingly rely on computational techniques to provide insight into the genome, proteome, and interactome data.

Robotics and computational biology are rapidly changing the way we formulate and test biological hypotheses. Advances in gene expression profiling by microarrays and protein profiling by mass spectrometry have suggested the potential to simultaneously view all genes expressed, all subsequent protein products, and all the interacting partners of each individual protein within a biological system. We can rapidly and accurately measure the relative activity of genes and proteins in normal and diseased tissue.

Diverse computational techniques have been applied to solve biological and medical problems over the years. Increasingly, such systems face challenges that arise from the enormous increase in information complexity and volume in these domains. In addition, the pace of evolution of our understanding of underlying principles requires continuous updates to existing databases, as well as systems that support reasoning and knowledge discovery. Performing these changes manually is becoming the bottleneck of the successful application of computer science to biological and medical domains.