Published on Oct. 9, 2017

Abstract:Traditional biomedical experiments are designed to study a single cohort for a single disease using a single technology. By studying disease with such a narrow lens, researchers make discoveries that are not reproducible because they are not representative of the real heterogeneity of disease. By integrating data from over 40 studies and 7,000 patients, we establish a robust signature of disease which correlates with disease activity and persists across blood, tissue, and sorted cell populations. We compare relationships of 104 diseases based on molecular and clinical manifestations from 41,000 gene expression samples and 2 million patient records. Finally, we contextualize single-cell RNA-seq data with bulk gene expression profiles to understand the relationships of novel cell subsets to known cell populations and human disease. By integrating biomedical datasets, my work has enabled an unbiased and multi-scale understanding of disease.

Bio: Winston Haynes is a PhD candidate in biomedical informatics at Stanford University. His research focuses on developing methods to improve understanding of disease through unbiased analyses of heterogeneous, publicly available data. Building off his discovery that publications are biased towards well-annotated genes instead of those with the strongest disease associations, his work integrates molecular and clinical evidence to identify overlooked aspects of disease, including therapeutically actionable relationships between seemingly disparate diseases and novel molecular pathways associated with disease activity