Computational Biology

Genome-wide association studies, minus the Winner’s Curse, are shown to be reliable and replicable

Columbia researchers de-bias the effects of the Winner’s Curse across a broad set of traits, finding that reporting deficiencies, not the paradigm of GWAS, are to blame for the failure to replicate.

David Knowles Wins NSF CAREER Award

Award supports computer scientist’s work to develop bioinformatics tools and methods to better understand RNA splicing’s role in ALS, cancer, and neuron development.

Machine learning applied to cancer: A PhD student doubles the number of breast cancer drivers

Felix Sanchez-Garcia, a computer science PhD student, describes a new algorithm that integrates data from multiple diverse sources to discover new breast cancer drivers.

About

The Computational Biology Group brings together interdisciplinary and cross-disciplinary individuals and skillsets to tackle problems in high throughput genomics, systems biology, and genetics.

The group develops computational methods to analyze high throughput data on genetic variants within species, primarily human SNP, and sequencing data.

The student and postdoc body in the group is very diverse in terms of undergraduate background as well as current PhD program or postdoctoral affiliation. The group meets weekly giving an opportunity for students to present their research internally and feedback on one another’s work. Weekly meetings occasionally give host to guest speakers, include a journal club, or extracurricular activities.