Cargando…
Mining significant high utility gene regulation sequential patterns
BACKGROUND: Mining frequent gene regulation sequential patterns in time course microarray datasets is an important mining task in bioinformatics. Although finding such patterns are of paramount important for studying a disease, most existing work do not consider gene-disease association during gene...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5751562/ https://www.ncbi.nlm.nih.gov/pubmed/29297335 http://dx.doi.org/10.1186/s12918-017-0475-4 |
_version_ | 1783289972345798656 |
---|---|
author | Zihayat, Morteza Davoudi, Heidar An, Aijun |
author_facet | Zihayat, Morteza Davoudi, Heidar An, Aijun |
author_sort | Zihayat, Morteza |
collection | PubMed |
description | BACKGROUND: Mining frequent gene regulation sequential patterns in time course microarray datasets is an important mining task in bioinformatics. Although finding such patterns are of paramount important for studying a disease, most existing work do not consider gene-disease association during gene regulation sequential pattern discovery. Moreover, they consider more absent/existence effects of genes during the mining process than taking the degrees of genes expression into account. Consequently, such techniques discover too many patterns which may not represent important information to biologists to investigate the relationships between the disease and underlying reasons hidden in gene regulation sequences. RESULTS: We propose a utility model by considering both the gene-disease association score and their degrees of expression levels under a biological investigation. We propose an efficient method called Top-HUGS, for discoverying significant high utility gene regulation sequential patterns from a time-course microarray dataset. CONCLUSIONS: In this study, the proposed methods were evaluated on a publicly available time course microarray dataset. The experimental results show higher accuracies compared to the baseline methods. Our proposed methods found that several new gene regulation sequential patterns involved in such patterns were useful for biologists and provided further insights into the mechanisms underpinning biological processes. To effectively work with the proposed method, a web interface is developed to our system using Java. To the best of our knowledge, this is the first demonstration for significant high utility gene regulation sequential pattern discovery. |
format | Online Article Text |
id | pubmed-5751562 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-57515622018-01-05 Mining significant high utility gene regulation sequential patterns Zihayat, Morteza Davoudi, Heidar An, Aijun BMC Syst Biol Research BACKGROUND: Mining frequent gene regulation sequential patterns in time course microarray datasets is an important mining task in bioinformatics. Although finding such patterns are of paramount important for studying a disease, most existing work do not consider gene-disease association during gene regulation sequential pattern discovery. Moreover, they consider more absent/existence effects of genes during the mining process than taking the degrees of genes expression into account. Consequently, such techniques discover too many patterns which may not represent important information to biologists to investigate the relationships between the disease and underlying reasons hidden in gene regulation sequences. RESULTS: We propose a utility model by considering both the gene-disease association score and their degrees of expression levels under a biological investigation. We propose an efficient method called Top-HUGS, for discoverying significant high utility gene regulation sequential patterns from a time-course microarray dataset. CONCLUSIONS: In this study, the proposed methods were evaluated on a publicly available time course microarray dataset. The experimental results show higher accuracies compared to the baseline methods. Our proposed methods found that several new gene regulation sequential patterns involved in such patterns were useful for biologists and provided further insights into the mechanisms underpinning biological processes. To effectively work with the proposed method, a web interface is developed to our system using Java. To the best of our knowledge, this is the first demonstration for significant high utility gene regulation sequential pattern discovery. BioMed Central 2017-12-14 /pmc/articles/PMC5751562/ /pubmed/29297335 http://dx.doi.org/10.1186/s12918-017-0475-4 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Zihayat, Morteza Davoudi, Heidar An, Aijun Mining significant high utility gene regulation sequential patterns |
title | Mining significant high utility gene regulation sequential patterns |
title_full | Mining significant high utility gene regulation sequential patterns |
title_fullStr | Mining significant high utility gene regulation sequential patterns |
title_full_unstemmed | Mining significant high utility gene regulation sequential patterns |
title_short | Mining significant high utility gene regulation sequential patterns |
title_sort | mining significant high utility gene regulation sequential patterns |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5751562/ https://www.ncbi.nlm.nih.gov/pubmed/29297335 http://dx.doi.org/10.1186/s12918-017-0475-4 |
work_keys_str_mv | AT zihayatmorteza miningsignificanthighutilitygeneregulationsequentialpatterns AT davoudiheidar miningsignificanthighutilitygeneregulationsequentialpatterns AT anaijun miningsignificanthighutilitygeneregulationsequentialpatterns |