Title: | A simple in silico approach to generate gene-expression profiles from subsets of cancer genomics data |
---|
Authors: | ID Khurshed, Mohammed (Author) ID Molenaar, Remco J. (Author) ID Noorden, Cornelis J. F. van (Author) |
Files: | PDF - Presentation file, download (2,04 MB) MD5: 269A8E27D0E5EF5B7D033CFA1F7B7D63
URL - Source URL, visit https://doi.org/10.2144/btn-2018-0179
|
---|
Language: | English |
---|
Typology: | 1.01 - Original Scientific Article |
---|
Organization: | NIB - National Institute of Biology
|
---|
Abstract: | In biomedical research, large-scale profiling of gene expression has become routine and offers a valuable means to evaluate changes in onset and progression of diseases, in particular cancer. An overwhelming amount of cancer genomics data has become publicly available, and the complexity of these data makes it a challenge to perform in silico data exploration, integration and analysis, in particular for scientists lacking a background in computational programming or informatics. Many web interface tools make these large datasets accessible but are limited to process large datasets. To accelerate the translation of genomic data into new insights, we provide a simple method to explore and select data from cancer genomic datasets to generate gene-expression profiles of subsets that are of specific genetic, biological or clinical interest. |
---|
Keywords: | cancer genomics, cBioPortal, data mining, epigenetics, gene expression, in silico |
---|
Publication status: | Published |
---|
Publication version: | Version of Record |
---|
Publication date: | 23.11.2019 |
---|
Year of publishing: | 2019 |
---|
Number of pages: | str. 172-176 |
---|
Numbering: | Vol. 67, no. 4 |
---|
PID: | 20.500.12556/DiRROS-19596 |
---|
UDC: | 577.2 |
---|
ISSN on article: | 0736-6205 |
---|
DOI: | 10.2144/btn-2018-0179 |
---|
COBISS.SI-ID: | 5289039 |
---|
Publication date in DiRROS: | 24.07.2024 |
---|
Views: | 386 |
---|
Downloads: | 558 |
---|
Metadata: | |
---|
:
|
Copy citation |
---|
| | | Share: | |
---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |