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 post-blockbuster drugs and to combat the healthcare cost explosion by increasing its quality at reduced expenses. 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 healthcare delivery. GeneSim\texttrademark, a learning technology platform dedicated to support genomic and molecular medicine, is introduced as an example of how intelligent computing can help boosting 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 to hunt molecular targets of drugs and contrast agents for molecular imaging. In silico drug application and RNAi 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 at reduced side-effects.
Search
Reviews
[Write a review of this article]
Find related articles from these CiteULike users
