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Cited by 49Alzheimer's disease research and treatmentsBioinformatics and Genomic NetworksDementia and Cognitive Impairment Research

Machine learning prediction and tau-based screening identifies potential Alzheimer’s disease genes relevant to immunity

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Binder, Jessica, Ursu, Oleg, Bologa, Cristian, Jiang, Shanya, Maphis, Nicole, Dadras, Somayeh, Chisholm, Devon, Weick, Jason +5 more·2022·Communications Biology
DOI 10.1038/s42003-022-03068-7PMC PMC8837797
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Abstract

With increased research funding for Alzheimer’s disease (AD) and related disorders across the globe, large amounts of data are being generated. Several studies employed machine learning methods to understand the ever-growing omics data to enhance early diagnosis, map complex disease networks, or uncover potential drug targets. We describe results based on a Target Central Resource Database protein knowledge graph and evidence paths transformed into vectors by metapath matching. We extracted features between specific genes and diseases, then trained and optimized our model using XGBoost, termed MPxgb(AD). To determine our MPxgb(AD) prediction performance, we examined the top twenty predicted genes through an experimental screening pipeline. Our analysis identified potential AD risk genes: FRRS1, CTRAM, SCGB3A1, FAM92B/CIBAR2, and TMEFF2. FRRS1 and FAM92B are considered dark genes, while CTRAM, SCGB3A1, and TMEFF2 are connected to TREM2-TYROBP, IL-1β-TNFα, and MTOR-APP AD-risk nodes, suggesting relevance to the pathogenesis of AD.

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  • Previous studies have demonstrated the superior performance of weighted methods as compared to resampling methods in addressing class imbalance.

    “previous studies have demonstrated the superior performance of weighted methods as compared to resampling methods in addressing class imbalance38”

    From Machine learning models identify predictive features of patient mortality across dementia types (2024) · cites this paper as [38]

    The Results (Focus on Alzheimer’s disease) compare weighting with resampling positives in an imbalanced AD-gene training set and select weighting because it performed better on the test set (AUC-ROC 0.93 versus 0.62). The source reports one application, not a general finding across previous studies; the resampled method also had the higher cross-validation AUC-ROC (0.98 versus 0.91). (LLM, conf=0.86)

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References 102 · Cited by 49

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Authors
Binder, J., Ursu, O., Bologa, C., Jiang, S., Maphis, N., Dadras, S., Chisholm, D., Weick, J., Myers, O., Kumar, P., Yang, J. J., Bhaskar, K., & Oprea, T. I.
Affiliations
  • University of New Mexico (US)
  • Merck & Co., Inc., Rahway, NJ, USA (United States) (US)
  • University of Copenhagen (DK)
Corresponding author
  • Bhaskar, Kiran
  • Oprea, Tudor I.
ORCID
Venue
Communications Biology
Publisher
Springer Science and Business Media LLC
Published
2022-02-11
Volume
5
Issue
1
Pages
125-125
ISSN
2399-3642 (ISSN-L)
DOI
10.1038/s42003-022-03068-7
PMC
PMC8837797
OpenAlex
W4211123915
Cited by
49
References
102
Citations by year
2026: 7 · 2025: 11 · 2024: 17 · 2023: 7 · 2022: 7
FWCI
6.66
Type
journal article
Language
en
Open access
Licence
Locations
  • Communications Biology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Northwestern University (P30 AG013854)
  • National Institutes of Health (U24TR002278, CA224370, AG013854)
  • National Institute on Aging (AG-013854, P30AG013854)
  • National Cancer Institute (U24CA224370)
  • National Institute of General Medical Sciences (K12GM088021, P20GM121176)
  • National Institute of Neurological Disorders and Stroke (RF1NS083704, R21NS077089, R01NS083704, R21NS093442)
  • National Center for Advancing Translational Sciences
  • Common Fund
Topics
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
Keywords
Alzheimer's disease, risk genes, tau protein, immunity, XGBoost, SCGB3A1, TMEFF2, dark genes, genetic screening
MeSH terms
Machine Learning, Alzheimer Disease, Humans, Membrane Proteins, Neoplasm Proteins, Early Diagnosis

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