Section: Application Domains
Life Sciences
Participants : Yasmine Assess, Sid-Ahmed Benabderrahmane, Matthieu Chavent, Marie-Dominique Devignes, Léo Gemthio, Mehdi Kaytoue, Vincent Leroux, Nizar Messai, Bernard Maigret, Amedeo Napoli, Malika Smaïl-Tabbone, Yannick Toussaint.
- Knowledge discovery in life sciences
is a process for extracting knowledge units from large biological databases, e.g. collection of genes.
One of the major application domains currently investigated by the Orpailleur team is related to life sciences, with a particular emphasis on biology (bioinformatics), medicine, and chemistry. The understanding of biological systems provides complex problems for computer scientists, and, when problems are solved, solutions bring new ideas not only for biologists but also for computer scientists. Moreover, the team includes biologists, chemists, and a physician, making Orpailleur a very original INRIA team.
Knowledge discovery is gaining more and more interest and importance in life sciences for mining either homogeneous databases such as protein sequences or structures, heterogeneous databases for discovering interactions between genes and environment, or between genetic and phenotypic data, especially for public health and pharmacogenomics domains. The latter case appears to be one main challenge in knowledge discovery in biology and involves knowledge discovery from complex data and thus KDDK. The interactions between researchers in biology and researchers in computer science improve not only knowledge about systems in biology, chemistry, and medicine, but knowledge about computer science as well. Solving problems for biologists using KDDK methods may involve the design of specific modules that, in turn, leads to adaptations of the KDDK process, especially in the preparation of data and in the interpretation of the extracted units.
