Joachim Selbig mostly deals with Genetics, Arabidopsis, Data mining, Metabolomics and Gene. His Arabidopsis research is included under the broader classification of Biochemistry. His Data mining study integrates concerns from other disciplines, such as Missing data, Value, Multivariate mutual information, Pointwise mutual information and Variation of information.
Joachim Selbig has included themes like Proteomics, Mutant, Computational biology, Systems biology and Candidate gene in his Metabolomics study. His study on Computational biology also encompasses disciplines like
His primary scientific interests are in Computational biology, Genetics, Data mining, Metabolite and Gene. In his study, Drug resistance is strongly linked to Bioinformatics, which falls under the umbrella field of Computational biology. His research in Genetics intersects with topics in Biological network, Population genetics and Heterosis.
His studies deal with areas such as Missing data, Mutual information, Cluster analysis, Sample and Principal component analysis as well as Data mining. His Metabolite research entails a greater understanding of Biochemistry. Gene expression profiling, Gene expression, Regulation of gene expression, Arabidopsis and Transcriptional regulation are the core of his Gene study.
Genetics, Data mining, Computational biology, Bioinformatics and Arabidopsis are his primary areas of study. His work carried out in the field of Genetics brings together such families of science as Evolutionary biology, Biological network and Heterosis. His Data mining study combines topics in areas such as Computation, State, Genome scale and Coupling.
His study in Computational biology focuses on Metabolic network in particular. His study in the fields of Metabolomics under the domain of Bioinformatics overlaps with other disciplines such as Biomarker. His research integrates issues of Proteomics, Transcription and Botany in his study of Arabidopsis.
Data mining, Botany, Genome scale, Orders of magnitude and Computation are his primary areas of study. His Data mining study incorporates themes from Variation, Multivariate analysis and Principal component analysis. He combines subjects such as Partial least squares regression, Metabolism and Arabidopsis with his study of Botany.
His Genome scale research incorporates themes from Theoretical computer science, State, Constraint and Coupling.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes
Oliver Thimm;Oliver Bläsing;Yves Gibon;Axel Nagel.
Plant Journal (2004)
pcaMethods—a bioconductor package providing PCA methods for incomplete data
Wolfram Stacklies;Henning Redestig;Matthias Scholz;Dirk Walther.
The mutual information: detecting and evaluating dependencies between variables.
Ralph E. Steuer;Jürgen Kurths;Carsten O. Daub;Janko Weise.
european conference on computational biology (2002)
A Robot-Based Platform to Measure Multiple Enzyme Activities in Arabidopsis Using a Set of Cycling Assays: Comparison of Changes of Enzyme Activities and Transcript Levels during Diurnal Cycles and in Prolonged Darkness
Yves Gibon;Oliver E. Blaesing;Jan Hannemann;Petronia Carillo.
The Plant Cell (2004)
Extension of the Visualization Tool MapMan to Allow Statistical Analysis of Arrays, Display of Coresponding Genes, and Comparison with Known Responses
Björn Usadel;Axel Nagel;Oliver Thimm;Henning Redestig.
Plant Physiology (2005)
Metabolomics of temperature stress
Charles Guy;Fatma Kaplan;Joachim Kopka;Joachim Selbig.
Physiologia Plantarum (2007)
Starch as a major integrator in the regulation of plant growth
Ronan Sulpice;Eva-Theresa Pyl;Hirofumi Ishihara;Sandra Trenkamp.
Proceedings of the National Academy of Sciences of the United States of America (2009)
Metabolomic and transcriptomic stress response of Escherichia coli
Szymon Jozefczuk;Sebastian Klie;Gareth S Catchpole;Jedrzej Szymanski.
Molecular Systems Biology (2010)
The metabolic signature related to high plant growth rate in Arabidopsis thaliana
Rhonda C. Meyer;Matthias Steinfath;Jan Lisec;Martina Becher.
Proceedings of the National Academy of Sciences of the United States of America (2007)
Parallel analysis of transcript and metabolic profiles: a new approach in systems biology
Ewa Urbanczyk‐Wochniak;Alexander Luedemann;Joachim Kopka;Joachim Selbig.
EMBO Reports (2003)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: