Lisa Bang

TL;DR: The network approach applied in this study can be used to uncover interactions between diseases as a result of their shared, potentially pleiotropic SNPs, and advance clinical research and even clinical practice by accelerating the understanding of disease mechanisms on the basis of similar underlying genetic associations.

TL;DR: This study proposes an integrative framework to identify epigenetic interactions between methylation and miRNA associated with transcriptomic changes in bladder cancer and finds 120 genes associated with interactions between the two epigenomic components.

TL;DR: A kernel weighted l1-regularized regression model is proposed to incorporate tumor subtype information and further reveal gene regulations affected by different breast cancer subtypes and identified subtype-specific network structures based on the associations between gene expression and DNA methylation.

TL;DR: In this article, a knowledge-based binning approach was developed for rare-variant association analysis and then applied to structural neuroimaging endophenotypes related to late-onset Alzheimer's disease (LOAD).

TL;DR: This study developed a novel biological knowledge-based binning approach for rare-variant association analysis and then applied the approach to structural neuroimaging endophenotypes related to late-onset Alzheimer’s disease (LOAD).