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.
Christina Leslie, PhD
Associate Member, Computational Biology Program, SKI
Research FocusComputational biologist Christina Leslie focuses on developing machine learning algorithms for computational and systems biology.
EducationPhD, University of California, Berkeley
- Loeb GB, Khan AA, Canner D, Hiatt JB, Shendure J, Darnell RB, Leslie CS, Rudensky AY. Transcriptome-wide miR-155 Binding Map Reveals Widespread Noncanonical MicroRNA Targeting. Mol Cell. 2012 Dec 14;48(5):760-70. doi: 10.1016/j.molcel.2012.10.002. Epub 2012 Nov 8.
- Samstein RM, Arvey A, Josefowicz SZ, Peng X, Reynolds A, Sandstrom R, Neph S, Sabo P, Kim JM, Liao W, Li MO, Leslie C, Stamatoyannopoulos JA, Rudensky AY. Foxp3 exploits a pre-existent enhancer landscape for regulatory T cell lineage specification. Cell. 2012 Sep 28;151(1):153-66. doi: 10.1016/j.cell.2012.06.053.
- 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