Dr. Kechris' focuses on the development and application of statistical methods for analyzing -omics data sets from high throughput technologies. She has several focus areas: (1) analyzing transcription factor binding and miRNA data to study the regulation of transcription and post-transcriptional processing, (2) examining the genetic and epigenetic factors controlling gene expression, (3) exploring the metabolome and (4) integrating multiple omics data. Dr. Kechris collaborates with investigators studying chronic obstructive pulmonary disease in the COPDGene genetic epidemiology study, substance use disorders using animal models, and early life determinants of diabetes and obesity in children.
Areas of Expertise
- Data science
- Chronic obstructive pulmonary disease
Education, Licensure & Certifications
- PhD, Statistics, University of California Berkeley, 2003
- MA, Statistics, University of California Berkeley, 1999
- BS, Applied Mathematics, University of California Los Angeles, 1997
- Chancellor’s Teaching Recognition Award, University of Colorado Anschutz Medical Campus, 2020
- Fellow, American Statistical Association, 2019
- Excellence in Faculty Research Award, Colorado School of Public Health, 2017
- Chair, Section on Statistics in Genomics and Genetics, American Statistical Association
- Member, NIH Biodata Management and Analysis Study Section
- Associate Editor, Biometrics
- BIOS 7731 Advanced Mathematical Statistics I
- BIOS 7659 Statistical Methods in Genomics
- NIH/NHLBI, Multi-omic Networks Associated with COPD progression in TOPMed Cohorts, 2020-2025, Role: Multi-PI (Kechris, Bowler, Lange, Banaei-Kashani)
- NIH/NCI, Addressing Sparsity in Metabolomics Data Analysis, 2018-2022, Role: Multi-PI (Kechris, Ghosh)
- NIH/NHLBI, Biomarker of Lung Disease in African Americans, 2018-2022, Role: Multi-PI (Bowler, Kechris)
Publications and Presentations
- B. Vestal, C. Moore, E. Wynn, L. Saba, T. Fingerlin, K. Kechris (2020) MCMSeq: Bayesian hierarchical modeling of clustered and repeated measures RNA sequencing experiments. BMC Bioinformatics (1):375.
- E. Mastej, L. Gillenwater, Y. Zhuang, K. Pratte, R. Bowler and K. Kechris (2020) Identifying protein-metabolite networks associated with COPD phenotypes. Metabolites 10:124.
- T. Ghosh, W. Zhang, D. Ghosh and K. Kechris (2020) Predictive modeling for metabolomics Data. Methods Molecular Biology 2104:313-336.
- WJ. Shi, Y. Zhuang, P. Russell, B. Hobbs, M. Parker, P. Castaldi, P. Rudra, B. Vestal, C. Hersh, L. Saba and K. Kechris (2019) Unsupervised discovery of phenotype specific multi-omics networks. Bioinformatics 35:4336-4343.
- D. Reinhold, H. Pielke-Lombardo, S. Jacobson, D. Ghosh and K. Kechris (2019) Pre-analytic considerations for mass spectrometry-based untargeted metabolomics data. Methods Molecular Biology 1978:323-340.