Dr. Laura M. Saba is an Associate Professor of Bioinformatics in the Department of Pharmaceutical Sciences of the Skaggs School of Pharmacy and Pharmaceutical Sciences at the University of Colorado Anschutz Medical Campus. She also has a secondary appointment in the Department of Biostatistics and Informatics at the Colorado School of Public Health, she is part of the Computation Bioscience Program at CU Anschutz, she is a member of the Training Faculty for the Pharmacology Program within the CU School of Medicine and she is a fellow at the Institute for Behavioral Genetics at CU Boulder. She received her Bachelor of Science in Chemistry from the Colorado School of Mines and her PhD in Biostatistics from the University of Colorado Health Science Center. She has more than 20 years of experience applying state-of-the-art statistical methodology to health-related studies including genetic, genomic, and proteomic studies of complex traits. Her research focuses on applying systems genetics techniques to high-throughput data to build predictive models.
Dr. Laura M. Saba is an Associate Professor of Bioinformatics in the Department of Pharmaceutical Sciences of the Skaggs School of Pharmacy and Pharmaceutical Sciences at the University of Colorado Anschutz Medical Campus. She also has a secondary appointment in the Department of Biostatistics and Informatics at the Colorado School of Public Health, she is part of the Computation Bioscience Program at CU Anschutz, she is a member of the Training Faculty for the Pharmacology Program within the CU School of Medicine and she is a fellow at the Institute for Behavioral Genetics at CU Boulder. She received her Bachelor of Science in Chemistry from the Colorado School of Mines and her PhD in Biostatistics from the University of Colorado Health Science Center. She has more than 20 years of experience applying state-of-the-art statistical methodology to health-related studies including genetic, genomic, and proteomic studies of complex traits. Her research focuses on applying systems genetics techniques to high-throughput data to build predictive models.