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

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 me...

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Veröffentlicht in:Bioinformatics (Oxford, England) England), 2021-04, Vol.37 (3), p.429-430
Hauptverfasser: Li, Quanxue, Dai, Wentao, Liu, Jixiang, Sang, Qingqing, Li, Yi-Xue, Li, Yuan-Yuan
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container_end_page 430
container_issue 3
container_start_page 429
container_title Bioinformatics (Oxford, England)
container_volume 37
creator Li, Quanxue
Dai, Wentao
Liu, Jixiang
Sang, Qingqing
Li, Yi-Xue
Li, Yuan-Yuan
description 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. The source code and user's guide of DysRegSig are freely available at Github: https://github.com/SCBIT-YYLab/DysRegSig. Supplementary data are available at Bioinformatics online.
doi_str_mv 10.1093/bioinformatics/btaa688
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subjects Applications Notes
Humans
Machine Learning
Neoplasms - genetics
Software
title DysRegSig: an R package for identifying gene dysregulations and building mechanistic signatures in cancer
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