Michael Tessel
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Michael Tessel has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Gang Feng
Biomedical Informatics Center (NUBIC), Clinical and Translational Sciences Institute (NUCATS), Northwestern University, Chicago, IL, USA
1 shared publication - Nancy L. Krett
Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, USA,
1 shared publication - Pan Du
The Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL 60611, USA
1 shared publication - Simon M. Lin
Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, 60611, USA
1 shared publication - Steven T. Rosen
Department of Hematology/Hematopoietic Cell Transplantation, Duarte, USA
1 shared publication - Warren Alden Kibbe
Data Science and Strategy, National Cancer Institute, Bethesda, MD
1 shared publication
Latest Publications
- Open Access
- 1 January 2010
- Published by Springer Nature in BMC Research Notes
- Vol. 3 (1) , 10
A collection of bioconductor methods to visualize gene-list annotations
Gene-list annotations are critical for researchers to explore the complex relationships between genes and functionalities. Currently, the annotations of a gene list are usually summarized by a table or a barplot. As such, potentially biologically important complexities such as one gene belonging to multiple annotation categories are difficult to extract. We have devised explicit and efficient visualization methods that provide intuitive methods for interrogating the intrinsic connections between biological categories and genes.
- 1 January 2010
- Published by Springer Nature in BMC Research Notes
- Vol. 3 (1) , 10
Open AccessA collection of bioconductor methods to visualize gene-list annotations
Gene-list annotations are critical for researchers to explore the complex relationships between genes and functionalities. Currently, the annotations of a gene list are usually summarized by a table or a barplot. As such, potentially biologically important complexities such as one gene belonging to multiple annotation categories are difficult to extract. We have devised explicit and efficient visualization methods that provide intuitive methods for interrogating the intrinsic connections between biological categories and genes.