DysRegSig: an R package for identifying gene dysregulations and building mechanistic signatures in cancer.
Bioinformatics
; 37(3): 429-430, 2021 04 20.
Article
en En
| MEDLINE
| ID: mdl-32717036
ABSTRACT
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.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Programas Informáticos
/
Neoplasias
Límite:
Humans
Idioma:
En
Año:
2021
Tipo del documento:
Article