Computational & Systems Biology Program
The Christina Leslie Lab
Research
Christina Leslie, PhD
Member, Computational & Systems Biology Program
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.
Featured News
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.
In the Lab
Scientists Find Cancer Drivers Hiding in a New Place
New findings from researchers at the Sloan Kettering Institute suggest that cancer causes may be lurking in the molecule that bridges DNA and protein.
Making a Splash: Researchers Apply Face-Detection Technology to the Study of Genes
Taking a cue from smartphone technology, scientists are using face-recognition algorithms to improve RNA interference.
People
Christina Leslie, PhD
Member, Computational & Systems Biology Program
- Computational biologist Christina Leslie focuses on developing machine learning algorithms for computational and systems biology.
- PhD, University of California, Berkeley
- cleslie@cbio.mskcc.org
- Email Address
- 646-888-2762
- Office Phone
Members
Graduate Student
Research Associate
Research Associate
Graduate Student
Research Scholar
Research Scholar
Graduate Student
Graduate Student
Graduate Student
Graduate Student
Graduate Student
Graduate Student
Graduate Student
Graduate Student
Graduate Student
Graduate Student
Graduate Student
Research Scholar
Graduate Student
Research Associate
Research Scholar
Research Assistant
Graduate Student
Research Scholar
Research Fellow
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
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