Bioinformatics and Handwriting/Speech Reconition: Uncoventional Applications of Similarity Search Tools
Name
MEBCS003.pdf
Size
82.15 KB
Format
Adobe PDF
Checksum (MD5)
f868b7d6a5eb40bf31cce1a27d858809
Author(s)
Jensen, Kyle
Stephanopoulos, Gregory
Date Issued
January 2006
Series/Report no.
Molecular Engineering of Biological and Chemical Systems (MEBCS)
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.
Subjects
Machine learning
bioinformatics
amino acids
protein sequences
sequence alignment
FastA
voice
dynamic programming
handwriting
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