CELL LINE SEQUENCING, CELL LINE SUITABILITY EVALUATION, NETWORK ANALYSIS, TRANSLATIONAL RESEARCH, TEXT MINING; CELL LINE SUITABILITY EVALUATION; TRANSLATIONAL RESEARCH; TEXT MINING
Abstract :
[en] Cell lines are widely used in translational biomedical research to study the genetic basis of diseases. A major approach for experimental disease modeling are genetic perturbation experiments that aim to trigger selected cellular disease states. In this type of experiments it is crucial to ensure that the targeted disease- related genes and pathways are intact in the used cell line. In this work we are developing a framework which integrates genetic sequence information and disease- specific network analysis for evaluating disease-specific cell line suitability.
Research center :
- Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) Luxembourg Centre for Systems Biomedicine (LCSB): Experimental Neurobiology (Balling Group)
Disciplines :
Genetics & genetic processes Computer science
Author, co-author :
Biryukov, Maria ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Antony, Paul ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Krishna, Abhimanyu ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
May, Patrick ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Trefois, Christophe ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
External co-authors :
no
Language :
English
Title :
Evaluation of Cell Line Suitability for Disease Specific Perturbation Experiments.
Alternative titles :
[en] Evaluation of Cell Line Suitability for Disease Specific Perturbation Experiments.
Publication date :
20 February 2015
Event name :
European Conference on Data Analysis 2013
Event organizer :
The German Classification Society (GfKl) The French speaking Classification Society (SFC)
Event place :
Luxembourg, Luxembourg
Event date :
from 10 - 07 - 2013 to - 12 - 10 - 2013
Audience :
International
Main work title :
Data Science, Learning by Latent Structures, and Knowledge Discovery
Main work alternative title :
[en] Data Science, Learning by Latent Structures, and Knowledge Discovery
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