The Christina Leslie Lab

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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Pictured: Christina Leslie

Christina Leslie, PhD

Associate Member

Associate Professor

Research Focus

Computational biologist Christina Leslie focuses on developing machine learning algorithms for computational and systems biology.
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Education

  • PhD, University of California, Berkeley

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