Today’s biomedical research and practice operate in a world where data and knowledge sources are ubiquitous, complex, and diverse. At the same time, we face the challenge to provide new, innovative, and targeted postblockbuster drugs and to combat the health care cost explosion by increasing its quality at reduced expense. Bioinformatics and computational systems biology exploit intelligent and learning computing technologies to integrate heterogeneous data, to extract the biomedical information hidden in the data, to discover knowledge about normal and abnormal life processes, and to transform this knowledge into value added for pharmaceutical products and health care delivery. GeneSim™, a learning technology platform dedicated to supporting genomic and molecular medicine, is introduced as an example of how intelligent computing can help boost the biomedical world. Based on a context-sensitive knowledge base, GeneSim provides solutions for learning and predictive modeling of genotype-phenotype relationships, molecular pathways, aspects of cellular function, and their relationships with macroscopic disease states. Topological pathway analysis enables researchers to hunt molecular targets of drugs and contrast agents for molecular imaging. In silico drug application and RNAi (ribonucleic acid interference) experiments can be carried out to identify disease mechanisms and to assess the putative therapeutic efficiency and side effects of drugs, eventually reaching a “kill early” decision for investigational drugs. Pharmacogenomics is supported to stratify patients according to the genetic and molecular state of their disease, allowing for an individualized therapy with reduced side effects.
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Stetter, M., Nägele, A., Dejori, M. (2007). GeneSim™: Intelligent IT Platform for the Biomedical World. In: Schuster, A.J. (eds) Intelligent Computing Everywhere. Springer, London. https://doi.org/10.1007/978-1-84628-943-9_9
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