Discovering Weighted Patterns in Intron Sequences Using Self-Adaptive Harmony Search and Back-Propagation Algorithms

A hybrid self-adaptive harmony search and back-propagation mining system was proposed to discover weighted patterns in human intron sequences. By testing the weights under a lazy nearest neighbor classifier, the numerical results revealed the significance of these weighted patterns. Comparing these...

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Detalles Bibliográficos
Autores principales: Huang, Yin-Fu, Wang, Chia-Ming, Liou, Sing-Wu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3662175/
https://www.ncbi.nlm.nih.gov/pubmed/23737711
http://dx.doi.org/10.1155/2013/249034
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author Huang, Yin-Fu
Wang, Chia-Ming
Liou, Sing-Wu
author_facet Huang, Yin-Fu
Wang, Chia-Ming
Liou, Sing-Wu
author_sort Huang, Yin-Fu
collection PubMed
description A hybrid self-adaptive harmony search and back-propagation mining system was proposed to discover weighted patterns in human intron sequences. By testing the weights under a lazy nearest neighbor classifier, the numerical results revealed the significance of these weighted patterns. Comparing these weighted patterns with the popular intron consensus model, it is clear that the discovered weighted patterns make originally the ambiguous 5SS and 3SS header patterns more specific and concrete.
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spelling pubmed-36621752013-06-04 Discovering Weighted Patterns in Intron Sequences Using Self-Adaptive Harmony Search and Back-Propagation Algorithms Huang, Yin-Fu Wang, Chia-Ming Liou, Sing-Wu ScientificWorldJournal Research Article A hybrid self-adaptive harmony search and back-propagation mining system was proposed to discover weighted patterns in human intron sequences. By testing the weights under a lazy nearest neighbor classifier, the numerical results revealed the significance of these weighted patterns. Comparing these weighted patterns with the popular intron consensus model, it is clear that the discovered weighted patterns make originally the ambiguous 5SS and 3SS header patterns more specific and concrete. Hindawi Publishing Corporation 2013-05-08 /pmc/articles/PMC3662175/ /pubmed/23737711 http://dx.doi.org/10.1155/2013/249034 Text en Copyright © 2013 Yin-Fu Huang et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Huang, Yin-Fu
Wang, Chia-Ming
Liou, Sing-Wu
Discovering Weighted Patterns in Intron Sequences Using Self-Adaptive Harmony Search and Back-Propagation Algorithms
title Discovering Weighted Patterns in Intron Sequences Using Self-Adaptive Harmony Search and Back-Propagation Algorithms
title_full Discovering Weighted Patterns in Intron Sequences Using Self-Adaptive Harmony Search and Back-Propagation Algorithms
title_fullStr Discovering Weighted Patterns in Intron Sequences Using Self-Adaptive Harmony Search and Back-Propagation Algorithms
title_full_unstemmed Discovering Weighted Patterns in Intron Sequences Using Self-Adaptive Harmony Search and Back-Propagation Algorithms
title_short Discovering Weighted Patterns in Intron Sequences Using Self-Adaptive Harmony Search and Back-Propagation Algorithms
title_sort discovering weighted patterns in intron sequences using self-adaptive harmony search and back-propagation algorithms
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3662175/
https://www.ncbi.nlm.nih.gov/pubmed/23737711
http://dx.doi.org/10.1155/2013/249034
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