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
- Ets transcription factor GABP controls T cell homeostasis and immunity. Luo CT, Osmanbeyoglu HU, Do MH, Bivona MR, Toure A, Kang D, Xie Y, Leslie CS, Li MO. Nat Commun. 2017 Oct 20;8(1):1062. doi: 10.1038/s41467-017-01020-6.
- Chromatin states define tumour-specific T cell dysfunction and reprogramming. Philip M, Fairchild L, Sun L, Horste EL, Camara S, Shakiba M, Scott AC, Viale A, Lauer P, Merghoub T, Hellmann MD, Wolchok JD, Leslie CS, Schietinger A. Nature. 2017 May 25;545(7655):452-456. doi: 10.1038/nature22367. Epub 2017 May 17.
- 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