Our lab develops novel computational methods to study cellular biological systems from a global and data-driven perspective. We seek to exploit diverse, high-throughput functional and genomic data to understand the molecular networks underlying fundamental cellular processes, including regulation of transcription, pre-mRNA processing, signaling, and post-transcriptional gene silencing. Our algorithmic methods draw on machine learning, a computational field concerned with learning accurate, predictive models from noisy and high-dimensional data.

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Selected Achievements

Introduction of string kernel methodology for SVM classification of biological sequences

Development of algorithms for predictive modeling of gene regulation

First systems-level analyses of competition between microRNAs and between target transcripts

Christina Leslie

At Work: Computational Biologist Christina Leslie

In the field of computational biology, Christina Leslie has the opportunity to expand the impact of her work by connecting math to science.