Reliance upon rodent models in fundamental biomedical research is increasing due to technologies that allow the genetic engineering of inbred mouse strains. Through these genetic technologies and other established benefits for biomedical research success, there is a rodent-based experimental “system” that serves the researcher’s needs for whole organism experimentation. Furthermore, it is accepted that medical advances require an “animal model” as an essential aspect to ultimate success. Direct studies on humans are seen to be problematic due to significant heterogeneity and diversity in human populations. Modern bioinformatics, particularly the pattern recognition field, provides potential answers to the problem of overcoming such outbred diversity. Advanced statistical methods and machine learning algorithms developed by computer scientists, for example recursive partitioning and support vector machines (SVMs), applied to human biomedical data, provide a basis for an alternative system to mouse models through unravelling diversity and providing clues to guide investigations into human disease.
|Title of host publication||Proceedings of the 8th World Congress on Alternatives and Animal Use in the Life Sciences, Montreal 2011|
|Editors||Svon Aulock, F P Gmber, P Magr, C Rauter Gknimuluache|
|Place of Publication||Switzerland|
|Number of pages||6|
|Publication status||Published - 2012|
|Event||WC8 - Montreal, Canada|
Duration: 21 Aug 2011 → 25 Aug 2011
|Period||21/08/11 → 25/08/11|
Lidbury, B., & Richardson, A. (2012). A Pattern Recognition Bioinformatics Alternative System to Rodent Models in Fundamental Research. In S. Aulock, F. P. Gmber, P. Magr, & C. R. Gknimuluache (Eds.), Proceedings of the 8th World Congress on Alternatives and Animal Use in the Life Sciences, Montreal 2011 (Vol. 1, pp. 515-520). (ALTEX Proceedings; Vol. 1, No. 1). Switzerland: Springer.