The applicability of recurrent neural networks for biological sequence analysis.
Selection of machine learning techniques requires a certain sensitivity to the requirements of the problem. In particular, the problem can be made more tractable by deliberately using algorithms that are biased toward solutions of the requisite kind. In this paper, we argue that recurrent neural networks have a natural bias toward a problem domain of which biological sequence analysis tasks are a subset. We use experiments with synthetic data to illustrate this bias. We then demonstrate that this bias can be exploitable using a data set of protein sequences containing several classes of subcellular localization targeting peptides. The results show that, compared with feed forward, recurrent neural networks will generally perform better on sequence analysis tasks. Furthermore, as the patterns within the sequence become more ambiguous, the choice of specific recurrent architecture becomes more critical.
Hawkins
John
J
School of Information Technology and Electrical Engineering, University of Queensland, QLD 4072, Australia. jhawkins@itee.uq.edu.au
Bodén
Mikael
M
D000465:Algorithms; D000595:Amino Acid Sequence; D020869:Gene Expression Profiling / Q000379:methods*; D008969:Molecular Sequence Data; D005810:Multigene Family / Q000502:physiology; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D020539:Sequence Analysis, Protein / Q000379:methods*; D013347:Subcellular Fractions / Q000378:metabolism*
D011506:Proteins