His primary scientific interests are in Data mining, Cluster analysis, Software, Biological data and Gene. His work on Visualization as part of general Data mining research is often related to Quantification methods, thus linking different fields of science. The study incorporates disciplines such as Text mining, Heuristic and Transitive relation in addition to Cluster analysis.
He has researched Software in several fields, including Bioinformatics, Ensemble learning, Pre-MicroRNA, Algorithm and Data set. His work in Bioinformatics tackles topics such as Workflow which are related to areas like Computational biology. His work on Systems biology as part of general Computational biology research is frequently linked to Relational database management system, thereby connecting diverse disciplines of science.
Computational biology, Gene, Data mining, Systems biology and Genetics are his primary areas of study. His Computational biology study combines topics from a wide range of disciplines, such as Genome, Interactome and Gene regulatory network. His research in Data mining intersects with topics in Software, Set and Cluster analysis.
His study in Software is interdisciplinary in nature, drawing from both Information retrieval and Bioinformatics. His work deals with themes such as Data type and Biological data, which intersect with Cluster analysis. His studies in Systems biology integrate themes in fields like DNA microarray, Biological network, Data science and Protein–protein interaction.
Jan Baumbach mainly focuses on Computational biology, Systems medicine, Network medicine, Gene and Systems biology. His Computational biology research incorporates themes from Infectious disease, Exon, Interactome, Coronavirus and Gene regulatory network. His Exon research includes elements of Binding site and Protein–protein interaction.
His research integrates issues of Drug repositioning, Disease, Neuroscience, Data science and Big data in his study of Systems medicine. His biological study spans a wide range of topics, including Human interactome, KEGG, Signalling and WikiPathways : Pathways for the people. His Gene research is multidisciplinary, incorporating elements of Ensemble learning and Omics.
Computational biology, Network medicine, Systems medicine, Coronavirus and Interactome are his primary areas of study. He combines subjects such as RNA, RNA-binding protein, RNA splicing, Exon and Gene regulatory network with his study of Computational biology. The concepts of his Gene regulatory network study are interwoven with issues in Bioinformatics, Organism, Transcriptional regulation, Adaptation and Regulation of gene expression.
His study in Network medicine is interdisciplinary in nature, drawing from both Key, Analytics and Knowledge management. His Systems medicine study is focused on Systems biology in general. Jan Baumbach interconnects Precision medicine, Position paper, Drug repositioning, Viral life cycle and Interoperability in the investigation of issues within Systems biology.
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clusterMaker: a multi-algorithm clustering plugin for Cytoscape
John H Morris;Leonard Apeltsin;Aaron M Newman;Jan Baumbach.
BMC Bioinformatics (2011)
Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology
Gregor Sturm;Francesca Finotello;Florent Petitprez;Jitao David Zhang.
AltAnalyze and DomainGraph: analyzing and visualizing exon expression data
Dorothea Emig;Nathan Salomonis;Jan Baumbach;Thomas Lengauer.
Nucleic Acids Research (2010)
Graph-based analysis and visualization of experimental results with ONDEX
Jacob Köhler;Jan Baumbach;Jan Taubert;Michael Specht.
Comparing the performance of biomedical clustering methods
Christian Wiwie;Jan Baumbach;Richard Röttger.
Nature Methods (2015)
Mass-spectrometry-based draft of the Arabidopsis proteome.
Julia Mergner;Martin Frejno;Markus List;Michael Papacek.
Current breathomics--a review on data pre-processing techniques and machine learning in metabolomics breath analysis.
A Smolinska;A-Ch Hauschild;R R R Fijten;J W Dallinga.
Journal of Breath Research (2014)
Partitioning biological data with transitivity clustering.
Tobias Wittkop;Tobias Wittkop;Tobias Wittkop;Dorothea Emig;Sita J. Lange;Sven Rahmann.
Nature Methods (2010)
Osteogenesis depends on commissioning of a network of stem cell transcription factors that act as repressors of adipogenesis.
Alexander Rauch;Anders K Haakonsson;Jesper G S Madsen;Mette Larsen.
Nature Genetics (2019)
Evidence for reductive genome evolution and lateral acquisition of virulence functions in two Corynebacterium pseudotuberculosis strains.
Jerônimo C. Ruiz;Vívian D'Afonseca;Artur Silva;Amjad Ali.
PLOS ONE (2011)
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