Abstract
Today, a huge amount of DNA and protein sequences are available, but the growth of biological knowledge has not kept pace with the increasing data. Hence much more effective computational methods are required to reveal the inherent functional units in these sequences in the form of patterns that could be related back to the biological world and life science applications. Pattern discovery provides such a computational tool. This chapter provides a brief review of known pattern discovery techniques for sequence data and later presents a new pattern discovery framework capable of discovering statistically significant sequence patterns effectively without relying on any prior domain knowledge. In response to the “too many patterns” problem, our algorithm is able to remove redundant patterns including those attributed by their strong statistical significant sub-patterns. It hence renders a compact set of quality patterns making interpretation and further model development much easier. When applying to transcription factor binding site data, it obtains a relatively small set of patterns-14 out of 18 consensus binding sites are associated with our top ranking patterns.
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© 2011 Higher Education Press, Beijing and Springer-Verlag Berlin Heidelberg
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Wong, A.K.C., Zhuang, D., Li, G.C.L., Lee, ES.A. (2011). Pattern Discovery and Recognition in Sequences. In: Wang, P.S.P. (eds) Pattern Recognition, Machine Intelligence and Biometrics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22407-2_2
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DOI: https://doi.org/10.1007/978-3-642-22407-2_2
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