TY  - GEN
A1  - Perscheid, Cindy
A1  - Uflacker, Matthias
T1  - Integrating Biological Context into the Analysis of Gene Expression Data
T2  - Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference
N2  - 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.
KW  - Gene expression
KW  - Machine learning
KW  - Feature selection
KW  - Association rule mining
KW  - Biclustering
KW  - Knowledge bases
Y1  - 2019
SN  - 978-3-319-99608-0
SN  - 978-3-319-99607-3
U6  - https://doi.org/10.1007/978-3-319-99608-0_41
SN  - 2194-5357
SN  - 2194-5365
VL  - 801
SP  - 339
EP  - 343
PB  - Springer
CY  - Cham
ER  -