TY - GEN
T1 - Functional interpretation of gene sets
T2 - 5th International Conference on Man-Machine Interactions, ICMMI 2017
AU - Gruca, Aleksandra
AU - Jaksik, Roman
AU - Psiuk-Maksymowicz, Krzysztof
N1 - Publisher Copyright:
© 2018, Springer International Publishing AG.
PY - 2018
Y1 - 2018
N2 - Modern high-throughput technologies based on genome, transcriptome or proteome profiling provide abundance of data that needs to be processed, analyzed and, finally, interpreted. Effective and efficient analysis of data coming from molecular profiling is crucial for a detailed diagnosis, prognosis, and prediction of therapy outcome. Meaningful conclusions can be drawn only by the use of sophisticated methods for biomedical and molecular data analysis and interpretation. In this study we present the approach for functional interpretation of gene or protein sets with clusters of Gene Ontology terms. We analyze transcription profiles of human cell line K562 and we show that clustering allows grouping functionally related GO terms and therefore obtaining more concise and comprehensive description. By applying cluster-specific data aggregation tool we are able to calculate statistics for the individual clusters of GO terms and compare the number of differentially expressed genes between two sample pairs. The presented tool is implemented as a part of annotation module available on the BioTest remote platform for hypothesis testing and analysis of biomedical data.
AB - Modern high-throughput technologies based on genome, transcriptome or proteome profiling provide abundance of data that needs to be processed, analyzed and, finally, interpreted. Effective and efficient analysis of data coming from molecular profiling is crucial for a detailed diagnosis, prognosis, and prediction of therapy outcome. Meaningful conclusions can be drawn only by the use of sophisticated methods for biomedical and molecular data analysis and interpretation. In this study we present the approach for functional interpretation of gene or protein sets with clusters of Gene Ontology terms. We analyze transcription profiles of human cell line K562 and we show that clustering allows grouping functionally related GO terms and therefore obtaining more concise and comprehensive description. By applying cluster-specific data aggregation tool we are able to calculate statistics for the individual clusters of GO terms and compare the number of differentially expressed genes between two sample pairs. The presented tool is implemented as a part of annotation module available on the BioTest remote platform for hypothesis testing and analysis of biomedical data.
KW - BioTest platform
KW - Clustering
KW - DNA microarrays
KW - Functional interpretation
KW - Gene Ontology
KW - Molecular profiling
KW - Semantic similarity
UR - https://www.scopus.com/pages/publications/85030793197
U2 - 10.1007/978-3-319-67792-7_13
DO - 10.1007/978-3-319-67792-7_13
M3 - Conference contribution
AN - SCOPUS:85030793197
SN - 9783319677910
T3 - Advances in Intelligent Systems and Computing
SP - 125
EP - 136
BT - Man-Machine Interactions 5 - 5th International Conference on Man-Machine Interactions, ICMMI 2017
A2 - Gruca, Aleksandra
A2 - Czachorski, Tadeusz
A2 - Harezlak, Katarzyna
A2 - Kozielski, Stanislaw
A2 - Piotrowska, Agnieszka
A2 - Czachorski, Tadeusz
PB - Springer Verlag
Y2 - 3 October 2017 through 6 October 2017
ER -