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Bioinformatics and Handwriting/Speech Reconition: Uncoventional Applications of Similarity Search Tools

Author(s)
Jensen, Kyle; Stephanopoulos, Gregory
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Abstract
This work introduces two unconventional applications for sequence alignment algorithms outside the domain of bioinformatics: handwriting recognition and speech recognition. In each application we treated data samples, such as the path of a and written pen stroke, as a protein sequence and use the FastA sequence alignment tool to classify unknown data samples, such as a written character. That is, we handle the handwriting and speech recognition problems like the protein annotation problem: given a sequence of unknown function, we annotate the sequence via sequence alignment. This approach achieves classification rates of 99.65% and 93.84% for the handwriting and speech recognition respectively. In addition, we provide a framework for applying sequence alignment to a variety of other non–traditional problems.
Date issued
2006-01
URI
http://hdl.handle.net/1721.1/30381
Series/Report no.
Molecular Engineering of Biological and Chemical Systems (MEBCS)
Keywords
Machine learning, bioinformatics, amino acids, protein sequences, sequence alignment, FastA, voice, dynamic programming, handwriting

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