Dr. Maricel Kann

University of Maryland Baltimore County, Baltimore, MD, USA

Dr. Maricel Kann is an Associate Professor at the University of Maryland, Baltimore County. She received a B. Sc. degree in Chemistry and a graduate degree in Pharmaceutical Chemistry from the Universidad de la Republica in Montevideo (Uruguay), where she was a research assistant in the Quantum Chemistry Department. In 2001, she obtained a doctoral degree from the University of Michigan in Chemistry. Her thesis work under the guidance of Dr. Richard A. Goldstein focused on the theory, statistics and methods for protein sequence alignment. After completing her Ph.D., Dr. Kann joined the Structure group at the National Center for Biotechnology Information (NIH) as a postdoctoral fellow. In August 2007, she joined the Department of Biological Sciences at UMBC as an Assistant Professor. Dr. Kann's research focuses on developing new computational methodologies to identify the role of individual cancer and other disease mutations in the disease mechanisms.

Dr. Kann is one of the leading experts in the area of translational Bioinformatics and has chaired several international conference sessions at the Pacific Symposium on Biocomputing (PSB), the Intelligent Systems and Molecular Biology (ISMB), the American Medical Informatics Association (AMIA) Summit in Bioinformatics and is the co-organizer of the Translational Bioinformatics Conference. She is a member of AMIA, the American Association for the Advancement of Science and the International Society of Computational Biology.

Dr. Kann is an associate editor of the Journal of Computational Biology and PLOS computational Biology, an NIH/NLM study session member, a past advisory board member of the PubMedCentral National Committee and of the scientific advisory board of the UniProt consortium.


DAY 3: September 13, 2019 | Session 4 | 9:50 AM - 10:10 AM

Pathway Networks Generated From Human Disease Phenome

Maricel Kann, PhD, University of Maryland Baltimore County, Baltimore, MD, USA

Understanding the effect of human genetic variations on disease can provide insight into phenotype-genotype relationships, and has great potential for improving the effectiveness of personalized medicine. While some genetic markers linked to disease susceptibility have been identified, a large number are still unknown. Here, I present a pathway-based approach to extend disease-variant associations and find new molecular connections between genetic mutations and diseases. We used a compilation of over 80,000 human genetic variants with known disease associations along with the Unified Medical Language System (UMLS) to normalize variant phenotype terminologies. All variants were grouped by UMLS Medical Subject Heading (MeSH) identifiers to determine pathway enrichment in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. By linking KEGG pathways through underlying variant associations, we elucidated connections between the human genetic variant-based disease phenome and metabolic pathways, finding novel disease connections not otherwise detected through gene-level analysis. For instance, mutations in Noonan Syndrome and Essential Hypertension share common pathways. When looking at broader disease categories, our network analysis showed that, as expected, large complex diseases, such as cancers, are highly linked by their common pathways. We found Cardiovascular and Skin and Connective Tissue Diseases to have the highest number of common pathways. This study constitutes an important contribution to extending disease-variant connections and new molecular links between diseases. This analysis also provides the foundation to build novel disease-drug networks through their underlying common metabolic pathways, thus enabling new diagnostic and therapeutic interventions.