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.

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Metadaten
Author details:Cindy PerscheidORCiD, Matthias Uflacker
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
Completion year:2019
Release date:2021/05/04
Tag:Association rule mining; Biclustering; Feature selection; Gene expression; Knowledge bases; Machine learning
Volume:801
Page number: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