IlliGAL Blogging

BMC Bioinformatics and Microarray and Bioinformatics Blog report the use of the GANN for the detection of conserved features in DNA. According to study authors Robert G. Beiko and Robert L. Charlebois

GANN is a machine learning method designed with the complexities of transcriptional regulation in mind. The key principle is that regulatory regions are composed of features such as consensus strings, characterized binding sites, and DNA structural properties. GANN identifies these features in a set of sequences, and then identifies combinations of features that can differentiate between the positive set (sequences with known or putative regulatory function) and the negative set (sequences with no regulatory function). Once these features have been identified, they can be used to classify new sequences of unknown function.

• Artificial Neural Networks are used for pattern detection, because they can model complex interactions between input variables (i.e., the features). This can be potentially very important if the positive set contains different types of regulatory regions that must all be classified.
• The number of sequence encodings that can be generated is practically infinite, and even a reasonable number (a few hundred) are too much to present to the neural network at once. The Outer Genetic Algorithm (OGA) was designed to test different subsets from the pool of available representations, and generate new subsets using evolutionary operations.

Biological applications of genetic algorithms are deliciously circular (procedures inspired by nature used to understand natural procedures), and the BioGEC workshop at GECCO is a good place to meet key players and learn about recent work.