Tara Eicher is a doctoral fellow in the Informatics Core within NCATS’ Division of Preclinical Innovation, where she is developing novel network analysis and machine learning methods for multi-omics data integration. She has contributed to multiple projects at NCATS, including the IntLIM tool, the RaMP knowledgebase and the COVID-19 OpenData Portal. She currently is working on extending the IntLIM tool to build ensemble models for phenotype/outcome prediction from multi-omics data and leveraging RaMP-based knowledge graphs to extend metabolite sets of interest for pathway analysis. Her methods are being applied to multi-omic studies, including for lung function in childhood asthma and survival outcome in astrocytoma.
Eicher previously conducted research across several areas, employing ensemble methods in proteogenomics, phylogenetic clustering, self-organizing maps to associate chromatin accessibility shapes with regulatory elements, and support vector machines to associate protein profile with drug response.
Eicher received her Bachelor of Arts in mathematics and her Master of Science in computer science from Wichita State University. She currently is a Ph.D. candidate at The Ohio State University.
Eicher’s goal is to become a principal investigator in an academic setting. She hopes to supervise a laboratory focused on methods development for clinical, translational or basic multi-omics research. Her goals during her fellowship include building her skills in professional networking, teamwork, scientific communication and project management. After completing her fellowship, Eicher plans to seek postdoctoral research associate positions. Although her work until the present has focused on the metabolome and transcriptome and (to a lesser extent) the epigenome, genome and proteome, she would like to expand her skillset to include analysis of the microbiome, electronic health records and medical imaging data.
- Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources
- Challenges in Proteogenomics: A Comparison of Analysis Methods with the Case Study of the DREAM Proteogenomics Sub-Challenge
- Self-Organizing Maps with Variable Neighborhoods Facilitate Learning of Chromatin Accessibility Signal Shapes Associated with Regulatory Elements