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EGID: an ensemble algorithm for improved genomic island detection in genomic sequences

Genomic islands (GIs) are genomic regions that are originally transferred from other organisms. The detection of genomic islands in genomes can lead to many applications in industrial, medical and environmental contexts. Existing computational tools for GI detection suffer either low recall or low p...

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Autores principales: Che, Dongsheng, Hasan, Mohammad Shabbir, Wang, Han, Fazekas, John, Huang, Jinling, Liu, Qi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Biomedical Informatics 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3280502/
https://www.ncbi.nlm.nih.gov/pubmed/22355228
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author Che, Dongsheng
Hasan, Mohammad Shabbir
Wang, Han
Fazekas, John
Huang, Jinling
Liu, Qi
author_facet Che, Dongsheng
Hasan, Mohammad Shabbir
Wang, Han
Fazekas, John
Huang, Jinling
Liu, Qi
author_sort Che, Dongsheng
collection PubMed
description Genomic islands (GIs) are genomic regions that are originally transferred from other organisms. The detection of genomic islands in genomes can lead to many applications in industrial, medical and environmental contexts. Existing computational tools for GI detection suffer either low recall or low precision, thus leaving the room for improvement. In this paper, we report the development of our Ensemble algorithm for Genomic Island Detection (EGID). EGID utilizes the prediction results of existing computational tools, filters and generates consensus prediction results. Performance comparisons between our ensemble algorithm and existing programs have shown that our ensemble algorithm is better than any other program. EGID was implemented in Java, and was compiled and executed on Linux operating systems. EGID is freely available at http://www5.esu.edu/cpsc/bioinfo/software/EGID.
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spelling pubmed-32805022012-02-21 EGID: an ensemble algorithm for improved genomic island detection in genomic sequences Che, Dongsheng Hasan, Mohammad Shabbir Wang, Han Fazekas, John Huang, Jinling Liu, Qi Bioinformation Prediction Model Genomic islands (GIs) are genomic regions that are originally transferred from other organisms. The detection of genomic islands in genomes can lead to many applications in industrial, medical and environmental contexts. Existing computational tools for GI detection suffer either low recall or low precision, thus leaving the room for improvement. In this paper, we report the development of our Ensemble algorithm for Genomic Island Detection (EGID). EGID utilizes the prediction results of existing computational tools, filters and generates consensus prediction results. Performance comparisons between our ensemble algorithm and existing programs have shown that our ensemble algorithm is better than any other program. EGID was implemented in Java, and was compiled and executed on Linux operating systems. EGID is freely available at http://www5.esu.edu/cpsc/bioinfo/software/EGID. Biomedical Informatics 2011-11-20 /pmc/articles/PMC3280502/ /pubmed/22355228 Text en © 2011 Biomedical Informatics This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.
spellingShingle Prediction Model
Che, Dongsheng
Hasan, Mohammad Shabbir
Wang, Han
Fazekas, John
Huang, Jinling
Liu, Qi
EGID: an ensemble algorithm for improved genomic island detection in genomic sequences
title EGID: an ensemble algorithm for improved genomic island detection in genomic sequences
title_full EGID: an ensemble algorithm for improved genomic island detection in genomic sequences
title_fullStr EGID: an ensemble algorithm for improved genomic island detection in genomic sequences
title_full_unstemmed EGID: an ensemble algorithm for improved genomic island detection in genomic sequences
title_short EGID: an ensemble algorithm for improved genomic island detection in genomic sequences
title_sort egid: an ensemble algorithm for improved genomic island detection in genomic sequences
topic Prediction Model
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3280502/
https://www.ncbi.nlm.nih.gov/pubmed/22355228
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