Prof. Yaron Orenstein

Bioinformatics and Computational Biology

Genetic sequencing produces vast amounts of data on DNA, RNA, and proteins. Hidden within this data are critical patterns on cellular function and disease processes, including gene regulation, and protein folding. The core challenge is to develop computational methods to identify these patterns, extract biological insights, and pave the way for new scientific discoveries.

Prof. Yaron Orenstein’s research focuses on developing computational methods for high-throughput biological data analysis. His research group works on genomic data analysis, neural network training, integrating heterogeneous data sources, and knowledge transfer between different experimental assays. In addition, the group develops methods for model interpretation to understand not only what a model predicts, but also the underlying biological principles.

Another key aspect of his research involves developing algorithms for the design of DNA, RNA, and protein sequences to optimize experiments and analyze Big Data. While some of these problems can be solved efficiently in polynomial time—often through reduction to graph-based problems—others are computationally difficult (NP-hard), requiring approximation methods and heuristics, such as integer linear programming.

Primary Research Areas:

• Bioinformatics and Computational Biology

• Genomic Data Analysis

• Deep Learning and Neural Networks

• Model Interpretation and Sequence Design (DNA, RNA, and Proteins)

Research Nature:

Theoretical-Applied, combining computational methods and algorithm development with software implementation and experimentation.

Career Path:

Graduates of the group acquire expertise in Data Science, computational biology, and algorithms, transitioning into key research roles in Biotech, Pharma, drug development, and synthetic biology.