In the last few decades, major advances in genomic technologies have led to an explosive growth in the amount of biological information available to the scientific community. This growth has revitalized the field of Bioinformatics to leverage this data for biological discoveries. In particular, ma…
In the last few decades, major advances in genomic technologies have led to an explosive growth in the amount of biological information available to the scientific community. This growth has revitalized the field of Bioinformatics to leverage this data for biological discoveries. In particular, machine learning has become a cornerstone of much research due to its ability to acquire models from data and employ these models for automatic inference and prediction. An ever growing number of machine learning methods are devoted to classifying biological sequences like DNA and protein sequences and annotate these sequences with novel functional information. On the other hand, evolutionary computing, which comprises randomized search and optimization techniques and comes in different flavors, such as Genetic Algorithms, Genetic Programming, and Evolutionary Strategies, has been garnering attention for solving challenging Bioinformatics problems.
This workshop will try to give an overview of various problem domains in Bioinformatics where machine learning, evolutionary algorithms, and their combination have been employed successfully. The focus of the workshop will be to introduce recent powerful hybrid methods that are able to address complex classification problems more accurately and efficiently through the combination of statistical machine learning techniques and evolutionary computing search strategies.
