Cargando…

DysRegSig: an R package for identifying gene dysregulations and building mechanistic signatures in cancer

SUMMARY: Dysfunctional regulations of gene expression programs relevant to fundamental cell processes can drive carcinogenesis. Therefore, systematically identifying dysregulation events is an effective path for understanding carcinogenesis and provides insightful clues to build predictive signature...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Quanxue, Dai, Wentao, Liu, Jixiang, Sang, Qingqing, Li, Yi-Xue, Li, Yuan-Yuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8058765/
https://www.ncbi.nlm.nih.gov/pubmed/32717036
http://dx.doi.org/10.1093/bioinformatics/btaa688
_version_ 1783681076641660928
author Li, Quanxue
Dai, Wentao
Liu, Jixiang
Sang, Qingqing
Li, Yi-Xue
Li, Yuan-Yuan
author_facet Li, Quanxue
Dai, Wentao
Liu, Jixiang
Sang, Qingqing
Li, Yi-Xue
Li, Yuan-Yuan
author_sort Li, Quanxue
collection PubMed
description SUMMARY: Dysfunctional regulations of gene expression programs relevant to fundamental cell processes can drive carcinogenesis. Therefore, systematically identifying dysregulation events is an effective path for understanding carcinogenesis and provides insightful clues to build predictive signatures with mechanistic interpretability for cancer precision medicine. Here, we implemented a machine learning-based gene dysregulation analysis framework in an R package, DysRegSig, which is capable of exploring gene dysregulations from high-dimensional data and building mechanistic signature based on gene dysregulations. DysRegSig can serve as an easy-to-use tool to facilitate gene dysregulation analysis and follow-up analysis. AVAILABILITY AND IMPLEMENTATION: The source code and user’s guide of DysRegSig are freely available at Github: https://github.com/SCBIT-YYLab/DysRegSig. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
format Online
Article
Text
id pubmed-8058765
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-80587652021-04-28 DysRegSig: an R package for identifying gene dysregulations and building mechanistic signatures in cancer Li, Quanxue Dai, Wentao Liu, Jixiang Sang, Qingqing Li, Yi-Xue Li, Yuan-Yuan Bioinformatics Applications Notes SUMMARY: Dysfunctional regulations of gene expression programs relevant to fundamental cell processes can drive carcinogenesis. Therefore, systematically identifying dysregulation events is an effective path for understanding carcinogenesis and provides insightful clues to build predictive signatures with mechanistic interpretability for cancer precision medicine. Here, we implemented a machine learning-based gene dysregulation analysis framework in an R package, DysRegSig, which is capable of exploring gene dysregulations from high-dimensional data and building mechanistic signature based on gene dysregulations. DysRegSig can serve as an easy-to-use tool to facilitate gene dysregulation analysis and follow-up analysis. AVAILABILITY AND IMPLEMENTATION: The source code and user’s guide of DysRegSig are freely available at Github: https://github.com/SCBIT-YYLab/DysRegSig. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2020-07-27 /pmc/articles/PMC8058765/ /pubmed/32717036 http://dx.doi.org/10.1093/bioinformatics/btaa688 Text en © The Author(s) 2020. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Li, Quanxue
Dai, Wentao
Liu, Jixiang
Sang, Qingqing
Li, Yi-Xue
Li, Yuan-Yuan
DysRegSig: an R package for identifying gene dysregulations and building mechanistic signatures in cancer
title DysRegSig: an R package for identifying gene dysregulations and building mechanistic signatures in cancer
title_full DysRegSig: an R package for identifying gene dysregulations and building mechanistic signatures in cancer
title_fullStr DysRegSig: an R package for identifying gene dysregulations and building mechanistic signatures in cancer
title_full_unstemmed DysRegSig: an R package for identifying gene dysregulations and building mechanistic signatures in cancer
title_short DysRegSig: an R package for identifying gene dysregulations and building mechanistic signatures in cancer
title_sort dysregsig: an r package for identifying gene dysregulations and building mechanistic signatures in cancer
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8058765/
https://www.ncbi.nlm.nih.gov/pubmed/32717036
http://dx.doi.org/10.1093/bioinformatics/btaa688
work_keys_str_mv AT liquanxue dysregsiganrpackageforidentifyinggenedysregulationsandbuildingmechanisticsignaturesincancer
AT daiwentao dysregsiganrpackageforidentifyinggenedysregulationsandbuildingmechanisticsignaturesincancer
AT liujixiang dysregsiganrpackageforidentifyinggenedysregulationsandbuildingmechanisticsignaturesincancer
AT sangqingqing dysregsiganrpackageforidentifyinggenedysregulationsandbuildingmechanisticsignaturesincancer
AT liyixue dysregsiganrpackageforidentifyinggenedysregulationsandbuildingmechanisticsignaturesincancer
AT liyuanyuan dysregsiganrpackageforidentifyinggenedysregulationsandbuildingmechanisticsignaturesincancer