Abstract
In this chapter we give an introduction to the area of bioinformatics handling the nucleotide sequence analysis problem. We give a brief introduction to the nature of DNA and RNA and use one of many topics - the Translation Initiation Start (TIS) problem - to explain a computational prediction of motifs on biological sequences. Correct identification of the Translation Initiation Start (TIS) in cDNA sequences is an important issue for genome annotation. Here we describe a computational method for TIS identification based in a combination of statistics and Artificial Neural Networks (ANNs). This method makes use of two modules, one sensitive to the conserved motif and the other sensitive to the coding/noncoding potential around the start codon. Finally by applying a method inspired by molecular biology, the simplified method of the ribosome scanning model improves the prediction significantly.
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Hatzigeorgiou, A., Megraw, M. (2006). Computational Analysis of Human DNA Sequences: An Application of Artificial Neural Networks. In: Pintér, J.D. (eds) Global Optimization. Nonconvex Optimization and Its Applications, vol 85. Springer, Boston, MA . https://doi.org/10.1007/0-387-30927-6_7
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DOI: https://doi.org/10.1007/0-387-30927-6_7
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-30408-3
Online ISBN: 978-0-387-30927-9
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