Literature DB >> 20628476

Biological Sequence Mining Using Plausible Neural Network and its Application to Exon/intron Boundaries Prediction.

Kuochen Li1, Dar-Jen Chang, Eric Rouchka, Yuan Yan Chen.   

Abstract

Biological sequence usually contains yet to find knowledge, and mining biological sequences usually involves a huge dataset and long computation time. Common tasks for biological sequence mining are pattern discovery, classification and clustering. The newly developed model, Plausible Neural Network (PNN), provides an intuitive and unified architecture for such a large dataset analysis. This paper introduces the basic concepts of the PNN, and explains how it is applied to biological sequence mining. The specific task of biological sequence mining, exon/intron prediction, is implemented by using PNN. The experimental results show the capability of solving biological sequence mining tasks using PNN.

Year:  2007        PMID: 20628476      PMCID: PMC2902184          DOI: 10.1901/jaba.2007.2007-165

Source DB:  PubMed          Journal:  Proc IEEE Symp Comput Intell Bioinforma Comput Biol


  2 in total

1.  Evaluation of gene structure prediction programs.

Authors:  M Burset; R Guigó
Journal:  Genomics       Date:  1996-06-15       Impact factor: 5.736

2.  Long-range sequence analysis in Xq28: thirteen known and six candidate genes in 219.4 kb of high GC DNA between the RCP/GCP and G6PD loci.

Authors:  E Y Chen; M Zollo; R Mazzarella; A Ciccodicola; C N Chen; L Zuo; C Heiner; F Burough; M Repetto; D Schlessinger; M D'Urso
Journal:  Hum Mol Genet       Date:  1996-05       Impact factor: 6.150

  2 in total

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