Home › Research › Digital health tools
Rapid Biomarker Screening of Alzheimer's Disease by Interpretable Machine Learning and Graphene-Assisted Raman Spectroscopy
Wang Z, Ye J, Zhang K, Ding L, Granzier-Nakajima T, Ranasinghe JC, Xue Y, Sharma S et al.. ACS nano, 2022-03-25
Topic: Amyloid, tau and blood-test diagnosis
The study of Alzheimer's disease (AD), the most common cause of dementia, faces challenges in terms of understanding the cause, monitoring the pathogenesis, and developing early diagnoses and effective treatments. Rapid and accurate identification of AD biomarkers in the brain is critical to providing key insights into AD and facilitating the development of early diagnosis methods. In this work, we developed a platform that enables a rapid screening of AD biomarkers by employing graphene-assisted Raman spectroscopy and machine learning interpretation in AD transgenic animal brains. Specifically, we collected Raman spectra on slices of mouse brains with and without AD and used machine learning to classify AD and non-AD spectra. By contacting monolayer graphene with the brain slices, the accuracy was increased from 77% to 98% in machine learning classification. Further, using a linear support vector machine (SVM), we identified a spectral feature importance map that reveals the importance of each Raman wavenumber in classifying AD and non-AD spectra. Based on this spectral feature importance map, we identified AD biomarkers including Aβ and tau proteins and other potential biomarkers, such as triolein, phosphatidylcholine, and actin, which have been confirmed by other biochemical studies. Our Raman-machine learning integrated method with interpretability will facilitate the study of AD and can be extended to other tissues and biofluids and for various other diseases.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Wang, Z., Ye, J., Zhang, K., Ding, L., Granzier-Nakajima, T., Ranasinghe, J. C., et al. (2022). Rapid Biomarker Screening of Alzheimer's Disease by Interpretable Machine Learning and Graphene-Assisted Raman Spectroscopy. ACS nano. https://doi.org/10.1021/acsnano.2c00538
Vancouver
Wang Z, Ye J, Zhang K, Ding L, Granzier-Nakajima T, Ranasinghe JC, et al. Rapid Biomarker Screening of Alzheimer's Disease by Interpretable Machine Learning and Graphene-Assisted Raman Spectroscopy. ACS nano. 2022. doi:10.1021/acsnano.2c00538. PMID: 35333038.
BibTeX
@article{wang2022-35333038,
author = {Wang, Z. and Ye, J. and Zhang, K. and Ding, L. and Granzier-Nakajima, T. and Ranasinghe, J. C. and Xue, Y. and Sharma, S. and others},
title = {{Rapid Biomarker Screening of Alzheimer's Disease by Interpretable Machine Learning and Graphene-Assisted Raman Spectroscopy}},
journal = {ACS nano},
year = {2022},
doi = {10.1021/acsnano.2c00538},
pmid = {35333038},
url = {https://doi.org/10.1021/acsnano.2c00538}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
Key findings
- Specifically, we collected Raman spectra on slices of mouse brains with and without AD and used machine learning to classify AD and non-AD spectra.
- By contacting monolayer graphene with the brain slices, the accuracy was increased from 77% to 98% in machine learning classification.
- Further, using a linear support vector machine (SVM), we identified a spectral feature importance map that reveals the importance of each Raman wavenumber in classifying AD and non-AD spectra.
- Based on this spectral feature importance map, we identified AD biomarkers including Aβ and tau proteins and other potential biomarkers, such as triolein, phosphatidylcholine, and actin, which have been confirmed by other biochemical studies.
- Our Raman-machine learning integrated method with interpretability will facilitate the study of AD and can be extended to other tissues and biofluids and for various other diseases.
Why it matters
This work used graphene-assisted Raman spectroscopy and machine learning to classify Alzheimer's and non-Alzheimer's brain tissue from transgenic mice, reaching 98% accuracy and identifying markers including Aβ and tau. Because it was done in animal brains, it is early-stage and does not yet provide a treatment, cure, or prevention for people.
New to this subject? Start with our plain-English guide: Digital health →
Study details
- Evidence strengthLow
How this study scored 49/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomized / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (ACS nano) |
| Journal's citation record | 4 / 4 | ACS Nano: journal h-index 566 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2022 — within 6 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomized / blinded | 0 / 2 | No randomization or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 4 / 7 | Cited 75 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 12.5 citations a year over 6 year(s) since 2022 |
| Impact for its field and year | 3 / 6 | Cited 6.2× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 2 other studies in this library |
| Still being cited | 3 / 4 | Cited 57 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Shengxi Huang: h-index 53 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | Harvard University: institution h-index 3,235 (OpenAlex) |
| Multicenter collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 35333038) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 49 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1021/acsnano.2c00538
- PubMed: 35333038
- Original source →
- Free full text: bioRxiv (Cold Spring Harbor Laboratory) →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Related
Studies in the library that share something with this one. The line above each title says what.
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.
Later studies that cite this one
2 studies in the alzhub library list this one in their references.
Related studies in Digital health tools
Also tagged “machine”
834 studies in the library carry the topic tag “machine”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 834 tagged studies.