TY  - JOUR
A1  - Perscheid, Cindy
T1  - Integrative biomarker detection on high-dimensional gene expression data sets
BT  - a survey on prior knowledge approaches
JF  - Briefings in bioinformatics
N2  - Gene expression data provide the expression levels of tens of thousands of genes from several hundred samples. These data are analyzed to detect biomarkers that can be of prognostic or diagnostic use. Traditionally, biomarker detection for gene expression data is the task of gene selection. The vast number of genes is reduced to a few relevant ones that achieve the best performance for the respective use case. Traditional approaches select genes based on their statistical significance in the data set. This results in issues of robustness, redundancy and true biological relevance of the selected genes. Integrative analyses typically address these shortcomings by integrating multiple data artifacts from the same objects, e.g. gene expression and methylation data. When only gene expression data are available, integrative analyses instead use curated information on biological processes from public knowledge bases. With knowledge bases providing an ever-increasing amount of curated biological knowledge, such prior knowledge approaches become more powerful. This paper provides a thorough overview on the status quo of biomarker detection on gene expression data with prior biological knowledge. We discuss current shortcomings of traditional approaches, review recent external knowledge bases, provide a classification and qualitative comparison of existing prior knowledge approaches and discuss open challenges for this kind of gene selection.
KW  - gene selection
KW  - external knowledge bases
KW  - biomarker detection
KW  - gene
KW  - expression
KW  - prior knowledge
Y1  - 2021
U6  - https://doi.org/10.1093/bib/bbaa151
SN  - 1467-5463
SN  - 1477-4054
VL  - 22
IS  - 3
PB  - Oxford Univ. Press
CY  - Oxford
ER  - 
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  - 
TY  - CHAP
A1  - Kurbel, Karl
A1  - Nowak, Dawid
A1  - Azodi, Amir
A1  - Jaeger, David
A1  - Meinel, Christoph
A1  - Cheng, Feng
A1  - Sapegin, Andrey
A1  - Gawron, Marian
A1  - Morelli, Frank
A1  - Stahl, Lukas
A1  - Kerl, Stefan
A1  - Janz, Mariska
A1  - Hadaya, Abdulmasih
A1  - Ivanov, Ivaylo
A1  - Wiese, Lena
A1  - Neves, Mariana
A1  - Schapranow, Matthieu-Patrick
A1  - Fähnrich, Cindy
A1  - Feinbube, Frank
A1  - Eberhardt, Felix
A1  - Hagen, Wieland
A1  - Plauth, Max
A1  - Herscheid, Lena
A1  - Polze, Andreas
A1  - Barkowsky, Matthias
A1  - Dinger, Henriette
A1  - Faber, Lukas
A1  - Montenegro, Felix
A1  - Czachórski, Tadeusz
A1  - Nycz, Monika
A1  - Nycz, Tomasz
A1  - Baader, Galina
A1  - Besner, Veronika
A1  - Hecht, Sonja
A1  - Schermann, Michael
A1  - Krcmar, Helmut
A1  - Wiradarma, Timur Pratama
A1  - Hentschel, Christian
A1  - Sack, Harald
A1  - Abramowicz, Witold
A1  - Sokolowska, Wioletta
A1  - Hossa, Tymoteusz
A1  - Opalka, Jakub
A1  - Fabisz, Karol
A1  - Kubaczyk, Mateusz
A1  - Cmil, Milena
A1  - Meng, Tianhui
A1  - Dadashnia, Sharam
A1  - Niesen, Tim
A1  - Fettke, Peter
A1  - Loos, Peter
A1  - Perscheid, Cindy
A1  - Schwarz, Christian
A1  - Schmidt, Christopher
A1  - Scholz, Matthias
A1  - Bock, Nikolai
A1  - Piller, Gunther
A1  - Böhm, Klaus
A1  - Norkus, Oliver
A1  - Clark, Brian
A1  - Friedrich, Björn
A1  - Izadpanah, Babak
A1  - Merkel, Florian
A1  - Schweer, Ilias
A1  - Zimak, Alexander
A1  - Sauer, Jürgen
A1  - Fabian, Benjamin
A1  - Tilch, Georg
A1  - Müller, David
A1  - Plöger, Sabrina
A1  - Friedrich, Christoph M.
A1  - Engels, Christoph
A1  - Amirkhanyan, Aragats
A1  - van der Walt, Estée
A1  - Eloff, J. H. P.
A1  - Scheuermann, Bernd
A1  - Weinknecht, Elisa
ED  - Meinel, Christoph
ED  - Polze, Andreas
ED  - Oswald, Gerhard
ED  - Strotmann, Rolf
ED  - Seibold, Ulrich
ED  - Schulzki, Bernhard
T1  - HPI Future SOC Lab
BT  - Proceedings 2015
N2  - Das Future SOC Lab am HPI ist eine Kooperation des Hasso-Plattner-Instituts mit verschiedenen Industriepartnern. Seine Aufgabe ist die Ermöglichung und Förderung des Austausches zwischen Forschungsgemeinschaft und Industrie.
Am Lab wird interessierten Wissenschaftlern eine Infrastruktur von neuester Hard- und Software kostenfrei für Forschungszwecke zur Verfügung gestellt. Dazu zählen teilweise noch nicht am Markt verfügbare Technologien, die im normalen Hochschulbereich in der Regel nicht zu finanzieren wären, bspw. Server mit bis zu 64 Cores und 2 TB Hauptspeicher. Diese Angebote richten sich insbesondere an Wissenschaftler in den Gebieten Informatik und Wirtschaftsinformatik. Einige der Schwerpunkte sind Cloud Computing, Parallelisierung und In-Memory Technologien. 
In diesem Technischen Bericht werden die Ergebnisse der Forschungsprojekte des Jahres 2015 vorgestellt.  Ausgewählte Projekte stellten ihre Ergebnisse am 15. April 2015 und 4. November 2015 im Rahmen der Future SOC Lab Tag Veranstaltungen vor.
KW  - Future SOC Lab
KW  - Forschungsprojekte
KW  - Multicore Architekturen
KW  - In-Memory Technologie
KW  - Cloud Computing
KW  - maschinelles Lernen
KW  - künstliche Intelligenz
Y1  - 2017
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-102516
ER  -