The applicability of recurrent neural networks for biological sequence analysis

Hawkins, J. and Boden, M. (2005) The applicability of recurrent neural networks for biological sequence analysis. IEEE-ACM Transactions on Computational Biology and Bioinformatiocs, 2 3: 243-253. doi:10.1109/TCBB.2005.44

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Author Hawkins, J.
Boden, M.
Title The applicability of recurrent neural networks for biological sequence analysis
Journal name IEEE-ACM Transactions on Computational Biology and Bioinformatiocs   Check publisher's open access policy
ISSN 1545-5963
Publication date 2005
Sub-type Article (original research)
DOI 10.1109/TCBB.2005.44
Volume 2
Issue 3
Start page 243
End page 253
Total pages 11
Editor D. Gusfidd
C. X. Ling
W. S. Noble
Q. Yang
Place of publication New York, N.Y. U.S.A.
Publisher IEEE Computer Society
Collection year 2005
Language eng
Subject C1
280207 Pattern Recognition
780101 Mathematical sciences
Abstract 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.
Keyword No Category
Machine Learning
Neural Network Architecture
Recurrent Neural Network
Bias
Biological Sequence Analysis
Motif
Subcellular Localization
Pattern Recognition
Classifier Design
Protein Secondary Structure
Architectural Bias
Prediction
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

 
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Citation counts: TR Web of Science Citation Count  Cited 14 times in Thomson Reuters Web of Science Article | Citations
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Created: Wed, 15 Aug 2007, 07:08:32 EST