Bioinformatics Workgroup

A natural key to fulfill these promises is the comprehensive mapping and study of genetic variations. Indeed, the focus of research from classical "static" -omics has moved towards their interrelation, and particularly towards the effect of variations. After the finishing of the draft of a "consensual" human genome, from 2003 the "variational" prefix is more and more implicitly understood before any -omics, and "variome" has become an -omics in itself, including single-nucleotide polymorphisms, copy-number variations, or even the methylome. A key challenge is the predictive modeling or cataloguizing the effects of genetic variations and their system/model-based integration in induction and explanation generation.

Nonetheless high-throughput methods still have not changed that biology and biomedicine are knowledge-rich sciences. The spectrum of knowledge starting from raw observations incorporates automatically generated results of data analysis, unintentionally formed patterns of co-occurrences of concepts on the web and in the literature, hypothesized models in peer-reviewed papers, manually curated knowledge bases, and codified text-book knowledge. The increasing weight of medical and clinical aspects with their multiple abstraction levels and the problems of pure, data-based inductive methods prompted new terms such as clinical genomics, and translational research, which indicates the "interpretational" bottleneck after the model-free, high-throughput methods. This bottleneck is one of the driving force behind the increasing appreciation of electronic background knowledge in the post-genomic era. A key challenge is the incorporation of knowledge in induction and interpretation.

In knowledge engineering such trends are the application of more and more powerful logics, voluminous ontologies, text-mining, information fusion, probabilistic knowledge representations, and Bayesian decision theory to formally link knowledge (beliefs) to actions. In inductive research such trends are the use of probabilistic domain models, Bayesian statistics, causal inference, learning with prior information, and knowledge-based learning. In informational technologies such recent trends are the uncertainty management in databases and on the word-wide WEB, and the more and more available high-performance and high-throughput computing using reconfigurable-, multicore/shared memory- and grid-architectures.