Summary of Graph-based Biomarker Discovery and Interpretation For Alzheimer’s Disease, by Maryam Khalid et al.
Graph-Based Biomarker Discovery and Interpretation for Alzheimer’s Disease
by Maryam Khalid, Fadeel Sher Khan, John Broussard, Arko Barman
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Quantitative Methods (q-bio.QM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning framework called BRAIN is introduced to optimize diagnostic accuracy and biomarker discovery for Alzheimer’s Disease (AD) diagnosis. The current approaches based on radiological imaging are limited due to high costs and availability. Blood tests have shown promise in diagnosing AD, and this framework aims to identify relevant biomarkers that can be used as drug targets for AD management. Using a holistic graph-based representation for biomarkers, BRAIN reveals three novel biomarker sub-networks whose interactions vary between the control and AD groups. This framework offers a new paradigm for drug discovery and biomarker analysis for AD. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Alzheimer’s Disease (AD) is a serious condition that affects many people worldwide. Diagnosing AD early and finding ways to treat it are crucial goals for scientists. Right now, doctors rely on tests like MRI scans, which can be expensive and hard to get. Recently, blood tests have shown promise in diagnosing AD, making them more accessible and cost-effective. This paper introduces a new machine learning framework called BRAIN that helps find the right biomarkers (tiny signs) in the blood that are connected to AD. By analyzing these biomarkers, scientists can discover new ways to diagnose and treat AD. |
Keywords
* Artificial intelligence * Machine learning
