Andrew Bordner
About Andrew Bordner
Andrew Bordner is a research scientist with advanced degrees in philosophy and science from the University of Wisconsin - Madison and Northern Illinois University, respectively. He specializes in computational methods for protein modeling and has made significant contributions to machine learning applications in medical outcomes and cancer research.
Work at Ridge
Andrew Bordner serves as a Research Scientist, focusing on advanced computational methods in the field of molecular biology and bioinformatics. His work involves developing algorithms and models that enhance the understanding of protein structures and their functions. He collaborates with various experts, including surgeons and experimentalists, to apply machine learning techniques in predicting surgical outcomes and validating computational predictions.
Education and Expertise
Andrew Bordner earned his Doctor of Philosophy from the University of Wisconsin - Madison, where he specialized in computational methods in biology. He also holds a Bachelor of Science degree from Northern Illinois University. His educational background provides a strong foundation for his expertise in machine learning, computational modeling, and protein structure analysis.
Background
Andrew Bordner has a robust academic and research background in computational biology. His training has equipped him with the skills necessary to develop innovative algorithms and methods for analyzing biological data. His research interests lie at the intersection of machine learning and molecular modeling, particularly in understanding protein interactions and functions.
Achievements
Andrew Bordner has made significant contributions to the field of computational biology. He developed a new sparse learning algorithm for class II MHC epitope prediction and demonstrated the feasibility of using quantum chemistry calculations to improve protein energy functions. His work includes discovering peptide inhibitors of the secretin receptor and creating a novel computational method for detecting driver mutations in cancer sequencing data.
Research Contributions
Andrew Bordner has pioneered the application of Factor Graphs and approximate inference methods in computational phylogenetics. He derived a probabilistic model of protein evolution that incorporates structural constraints, showcasing his innovative approach to understanding biological processes through computational techniques.