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.

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.

Crowdsourced family tree yields new insights about humanity

A study published in Science describes how researchers created, from 86M genealogy profiles, population-scale family trees to reassess longevity and marriage and migration patterns. Yaniv Erlich is the study’s senior author.

About

Computational biology and bioinformatics involve development and application of analysis methods for high throughput experimental data in molecular biology to facilitate biomedical research. Active topics of investigation in Columbia involve systems biology, biological networks, massively parallel (“next generation”) DNA sequencing, RNA sequencing, ChIP sequencing, analysis of transcription factor binding sites, single nucleotide polymorphisms, population genetics, molecular evolution, personalized medicine, tumor genomics.