ArticleJournal of proteome research2022
Exposing the Brain Proteomic Signatures of Alzheimer's Disease in Diverse Racial Groups: Leveraging Multiple Data Sets and Machine Learning.
Article in Journal of proteome research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 24 citations in OpenAlex.
- Development of Plasma Protein Classification Models for Alzheimer's Disease Using Multiple Machine Learning Approaches.International journal of molecular sciences · 2025Article
- MSIght: A Modular Platform for Improved Confidence in Global, Untargeted Mass Spectrometry Imaging Annotation.Journal of proteome research · 2025Article
- Small Heat Shock Proteins: Protein Aggregation Amelioration and Neuro- and Age-Protective Roles.International journal of molecular sciences · 2025Review
- Article
- Establishing Quality Control Metrics for Large-Scale Plasma Proteomic Sample Preparation.ACS measurement science au · 2024Article
- Skin Surface Sebum Analysis by ESI-MS.Biomolecules · 2024Review
- Large-scale Deep Proteomic Analysis in Alzheimer's Disease Brain Regions Across Race and Ethnicity.bioRxiv : the preprint server for biology · 2024Article
- Reactive astrocytes secrete the chaperone HSPB1 to mediate neuroprotection.Science advances · 2024Article
- Quantitative proteomics of cerebrospinal fluid from African Americans and Caucasians reveals shared and divergent changes in Alzheimer's disease.Molecular neurodegeneration · 2023Article
- Advances, obstacles, and opportunities for machine learning in proteomics.Cell reports. Physical science · 2022Article
- How (Not) to Generate a Highly Predictive Biomarker Panel Using Machine Learning.Journal of proteome research · 2022Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Recent studies have highlighted that the proteome can be used to identify potential biomarker candidates for Alzheimer's disease (AD) in diverse cohorts. Furthermore, the racial and ethnic background of participants is an important factor to consider to ensure the effectiveness of potential biomarkers for representative populations. A promising approach to survey potential biomarker candidates for diagnosing AD in diverse cohorts is the application of machine learning to proteomics data sets. Herein, we leveraged six existing bottom-up proteomics data sets, which included non-Hispanic White, African American/Black, and Hispanic participants, to study protein changes in AD and cognitively unimpaired participants. Machine learning models were applied to these data sets and resulted in the identification of amyloid-β precursor protein (APP) and heat shock protein β-1 (HSPB1) as two proteins that have high ability to distinguish AD; however, each protein's performance varied based upon the racial and ethnic background of the participants. HSPB1 particularly was helpful for generating high areas under the curve (AUCs) for African American/Black participants. Overall, HSPB1 improved the performance of the machine learning models when combined with APP and/or participant age and is a potential candidate that should be further explored in AD biomarker discovery efforts.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.