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
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