L
Lisa Bang
Researcher at Geisinger Health System
Publications - 9
Citations - 152
Lisa Bang is an academic researcher from Geisinger Health System. The author has contributed to research in topics: Epigenomics & DNA methylation. The author has an hindex of 6, co-authored 8 publications receiving 108 citations.
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Journal ArticleDOI
Human-Disease Phenotype Map Derived from PheWAS across 38,682 Individuals
Anurag Verma,Lisa Bang,Jason E. Miller,Yanfei Zhang,Ming Ta Michael Lee,Yu Zhang,Marta Byrska-Bishop,David J. Carey,Marylyn D. Ritchie,Sarah A. Pendergrass,Dokyoon Kim +10 more
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.
Journal ArticleDOI
Identification of epigenetic interactions between miRNA and DNA methylation associated with gene expression as potential prognostic markers in bladder cancer
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.
Journal ArticleDOI
Identifying subtype-specific associations between gene expression and DNA methylation profiles in breast cancer.
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.
Journal ArticleDOI
Knowledge-driven binning approach for rare variant association analysis: application to neuroimaging biomarkers in Alzheimer's disease.
Dokyoon Kim,Dokyoon Kim,Anna O. Basile,Lisa Bang,Emrin Horgusluoglu,Seunggeun Lee,Marylyn D. Ritchie,Marylyn D. Ritchie,Andrew J. Saykin,Kwangsik Nho +9 more
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).
Knowledge-driven binning approach for rare variant association analysis: application to neuroimaging biomarkers in Alzheimer's disease
Dokyoon Kim,Dokyoon Kim,Anna O. Basile,Lisa Bang,Emrin Horgusluoglu,Seunggeun Lee,Marylyn D. Ritchie,Marylyn D. Ritchie,Andrew J. Saykin,Kwangsik Nho +9 more
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).