Integrating Biological Context into the Analysis of Gene Expression Data
- High-throughput RNA sequencing produces large gene expression datasets whose analysis leads to a better understanding of diseases like cancer. The nature of RNA-Seq data poses challenges to its analysis in terms of its high dimensionality, noise, and complexity of the underlying biological processes. Researchers apply traditional machine learning approaches, e. g. hierarchical clustering, to analyze this data. Until it comes to validation of the results, the analysis is based on the provided data only and completely misses the biological context. However, gene expression data follows particular patterns - the underlying biological processes. In our research, we aim to integrate the available biological knowledge earlier in the analysis process. We want to adapt state-of-the-art data mining algorithms to consider the biological context in their computations and deliver meaningful results for researchers.
Author details: | Cindy PerscheidORCiDGND, Matthias Uflacker |
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DOI: | https://doi.org/10.1007/978-3-319-99608-0_41 |
ISBN: | 978-3-319-99608-0 |
ISBN: | 978-3-319-99607-3 |
ISSN: | 2194-5357 |
ISSN: | 2194-5365 |
Title of parent work (English): | Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference |
Publisher: | Springer |
Place of publishing: | Cham |
Publication type: | Other |
Language: | English |
Date of first publication: | 2019/01/09 |
Publication year: | 2019 |
Release date: | 2021/05/04 |
Tag: | Association rule mining; Biclustering; Feature selection; Gene expression; Knowledge bases; Machine learning |
Volume: | 801 |
Number of pages: | 5 |
First page: | 339 |
Last Page: | 343 |
Organizational units: | An-Institute / Hasso-Plattner-Institut für Digital Engineering gGmbH |
DDC classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke |
Peer review: | Referiert |