Artificial Neural Networks for Molecular Sequence Analysis
Cathy H. Wu
Computers & Chemistry , 21(4),
237-256 (1997).
Abstract
Artificial neural networks provide a unique computing
architecture whose potential has attracted interest
from researchers across different disciplines. As a
technique for computational analysis, neural network
technology is very well suited for the analysis of
molecular sequence data. It has been applied successfully
to a variety of problems, ranging from gene
identification, to protein structure prediction and
sequence classification. This article provides an overview
of major neural network paradigms, discusses design
issues, and reviews current applications in DNA/RNA
and protein sequence analysis.
