Improved access to sequential motifs: a note on the architectural bias of recurrent networks

For many biological sequence problems the available data occupies only sparse regions of the problem space. To use machine learning effectively for the analysis of sparse data we must employ architectures with an appropriate bias. By experimentation we show that the bias of recurrent neural networks-recently analyzed by Tino et al. and Hammer and Tino-offers superior access to motifs (sequential patterns) compared to the, in bioinformatics, standardly used feedforward neural networks.