Plenary lecture III: artificial intelligence methods in the interpretation of statistical testing of genes under hypothetical balancing selection
Krzysztof A. Cyran · 2008
The detection of natural selection at the molecular level is one of the crucial problems in contemporary population genetics. There exists a number of statistical tests designed for it, however the interpretation of the outcomes is often obscure, because of the existence of factors like: population growth, migration and recombination. The author has proposed the multi-null methodology, and he applied it for four genes implicated in human familial cancer: ATM, RECQL, WRN and BLM. Because of high computational effort required for estimating critical values under nonclassical nulls, mentioned methodology is not appropriate for selection screening. Therefore, the author in this plenary lecture presents novel, artificial intelligence based methodology, helpful in the interpretation of the tests outcomes applied only versus classical null hypotheses. This method does not require long-lasting simulations and, as it is shown in a lecture, it gives reliable results. As examples of artficial intelligence methods the rough set theory and artificail neural networks are used in the aforementioned problem.