Our group develops computational methods to study and model transcriptional, co-transcriptional, and post-transcriptional gene regulatory mechanisms and to dissect the dysregulation of gene expression in cancer. The techniques we use generally come from machine learning, a field at the intersection of computer science and statistics. In particular, we develop supervised learning methods -- algorithmic approaches for learning predictive models from data -- that we train on high-throughput experimental data, increasingly from next generation sequencing. As we try to advance the state-of-the-art in statistical and machine learning models of gene regulation, we also seek to drive biological discovery through close collaborations with experimentalists to study specific regulatory mechanisms.
- Affinity regression predicts the recognition code of nucleic acid-binding proteins. Pelossof R, Singh I, Yang JL, Weirauch MT, Hughes TR, Leslie CS. Nat Biotechnol. 2015.
- Early enhancer establishment and regulatory locus complexity shape transcriptional programs in hematopoietic differentiation. González AJ, Setty M, Leslie CS. Nat Genet. 2015.
- Linking signaling pathways to transcriptional programs in breast cancer. Osmanbeyoglu HU, Pelossof R, Bromberg JF, Leslie CS. Genome Res. 2014.
- Ubiquitously transcribed genes use alternative polyadenylation to achieve tissue-specific expression. Lianoglou S, Garg V, Yang JL, Leslie CS, and Mayr C. Genes and Development, 2013.
- Inferring transcriptional and microRNA-mediated regulatory programs in glioblastoma. Setty M, Helmy K, Khan AA, Silber J, Arvey A, Neezen F, Agius P, Huse JT, Holland EC, Leslie CS. Mol Syst Biol. 2012.
- Transcriptome-wide miR-155 binding map reveals widespread noncanonical microRNA targeting. Loeb GB, Khan AA, Canner D, Hiatt JB, Shendure J, Darnell RB, Leslie CS, Rudensky AY. Mol Cell, 2012.