Nima Aghaeepour

2 months ago | cell.com | Grace Ramey |Alice S. Tang |Thanaphong Phongpreecha |Monica Yang |Sarah R. Woldemariam |Tomiko Oskotsky | +6 more

Keywords autoimmunity Alzheimer’s bioinformatics case-control cohort electronic health records risk analysis sex differences statistical epidemiology Introduction Alzheimer’s disease (AD) is a debilitating neurodegenerative disease that is accompanied by enormous social and economic burdens, and its prevalence is increasing due to the growing aging population worldwide.1,2 AD is characterized biologically by amyloid plaques and tau deposition in the brain, while clinical syndromic diagnoses,...

Dec 9, 2024 | ejog.org | Danielle M. Panelli |Jonathan Mayo |Ronald J. Wong |Martin Becker |Dorien Feyaerts |Ivana Maric | +8 more

Dear Editorial Board for European Journal of Obstetrics & Gynecology and Reproductive Biology,The authors regret that a data error has been detected in the above referenced manuscript, which does not alter the findings of the study. However, we would like to issue an erratum to correct the data errors which were related to coding of two variables (race/ethnicity and parity).

Feb 20, 2024 | nature.com | Alice S. Tang |Jacquelyn Roger |Sarah R. Woldemariam |Riley Bove |Nima Aghaeepour |Tomiko Oskotsky | +1 more

AbstractIdentification of Alzheimer’s disease (AD) onset risk can facilitate interventions before irreversible disease progression. We demonstrate that electronic health records from the University of California, San Francisco, followed by knowledge networks (for example, SPOKE) allow for (1) prediction of AD onset and (2) prioritization of biological hypotheses, and (3) contextualization of sex dimorphism.

Aug 16, 2023 | nature.com | Thanaphong Phongpreecha |Neal G Ravindra |Samson Mataraso |Camilo A. Espinosa |Alan Chang |Martin Becker | +5 more

AbstractAssay for Transposase Accessible Chromatin by sequencing (ATAC-seq) accurately depicts the chromatin regulatory state and altered mechanisms guiding gene expression in disease. However, bulk sequencing entangles information from different cell types and obscures cellular heterogeneity. To address this, we developed Cellformer, a deep learning method that deconvolutes bulk ATAC-seq into cell type-specific expression across the whole genome.