Adam Ertel, PhD
233 S. Tenth Street, Suite 1009
Philadelphia, PA 19107
(215) 503-9142 fax
Most Recent Peer-reviewed Publications
- Genome-wide redistribution of MeCP2 in dorsal root ganglia after peripheral nerve injury
- Structure-based screen identifies a potent small molecule inhibitor of Stat5a/b with therapeutic potential for prostate cancer and chronic myeloid leukemia
- The endogenous cell-fate factor dachshund restrains prostate epithelial cell migration via repression of cytokine secretion via a CXCL signaling module
- RB loss contributes to aggressive tumor phenotypes in MYC-driven triple negative breast cancer
- Monocyte-macrophage differentiation of acute myeloid leukemia cell lines by small molecules identified through interrogation of the connectivity map database
PhD, Biomedical Engineering/Bioinformatics, Drexel University, Philadelphia, PA - 2008
BS, Electrical Engineering, University of Connecticut , CT
Expertise & Research Interests
Bioinformatics and pathway-based approaches to gene expression profiling.
Gene interactions and molecular profiles in cancer.
SNP genotype and copy number analysis for genome-wide association studies.
My research focuses on patterns of gene expression, gene regulation, and gene product interactions that can be inferred from large collections of mRNA expression data. This approach is useful for identifying normal interaction and regulatory connections between genes as well as the disruption of these connections in complex diseases such as cancer. Bioinformatics approaches allow these connections to be easily extended into the context of biological pathways in order to understand global changes with respect to disease states or treatment. I’ve collaborated extensively with other Principal Investigators in the KCC to establish automated analysis pipelines for genes, gene signatures, and interaction profiles that provide informative readouts of pathway function and dysfunction associated with disease states.
Future plans include the automation of a publically accessible web-based tool that provides a user-friendly readout of genes, gene signatures, and interaction profiles across multiple phenotypes, disease states, and therapeutic interventions. As this tool evolves, it will be useful for designing and implementing classification algorithms to assist disease diagnosis and guided therapy.