miRNA, transcriptional regulation and cognitive decline
By applying differential expression, WGCNA, as well as linear and non-linear machine learning approaches, we identify microRNA signatures that can help identify patients at distinct stages of disease progression, as well as signatures that can predict the course of the disease. These data are compared with phenotyping data, such as cognitive function and ATN biomarkers. We will also discuss the role of other non-coding RNAs besides microRNAs and provide a framework for developing RNA-based point-of-care assays.
Authors
- Ivana Delalle (ORCID: https://orcid.org/0000-0002-1873-3064)
- Andrew J. Saykin (ORCID: https://orcid.org/0000-0002-1376-8532)
- Farahnaz Sananbenesi (ORCID: https://orcid.org/0000-0002-2123-7694)
- André Fischer (ORCID: https://orcid.org/0000-0001-8546-1161)
- D. Krüger (ORCID: https://orcid.org/0000-0003-1950-9547)
- Kwangsik Nho (ORCID: https://orcid.org/0000-0002-7624-3872)
- Leslie M. Shaw (ORCID: https://orcid.org/0000-0002-7650-1210)
Institutions
- Boston University (US)
- German Center for Neurodegenerative Diseases (DE)
- Institute for Neurodegenerative Disorders (US)
- Indiana University School of Medicine
- Indiana University – Purdue University Indianapolis (US)
- Indiana University (US)
- University of Pennsylvania (US)
- Philadelphia University (US)
Publication Details
- Journal
- Alzheimer s & Dementia
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1002/alz.083400
- Primary Topic
- MicroRNA in disease regulation
- Type
- article
- Field-Weighted Citation Impact
- 0.00