Using machine intelligence to uncover Alzheimer’s disease progression heterogeneity
Open accessAbstract
Aim: Research suggests that Alzheimer’s disease (AD) is heterogeneous with numerous subtypes. Through a proprietary interactive ML system, several underlying biological mechanisms associated with AD pathology were uncovered. This paper is an introduction to emerging analytic efforts that can more precisely elucidate the heterogeneity of AD. Methods: A public AD data set (GSE84422) consisting of transcriptomic data of postmortem brain samples from healthy controls (n = 121) and AD (n = 380) subjects was analyzed. Data were processed by an artificial intelligence platform designed to discover potential drug repurposing candidates, followed by an interactive augmented intelligence program. Results: Using perspective analytics, six perspective classes were identified: Class I is defined by TUBB1, ASB4, and PDE5A; Class II by NRG2 and ZNF3; Class III by IGF1, ASB4, and GTSE1; Class IV is defined by cDNA FLJ39269, ITGA1, and CPM; Class V is defined by PDE5A, PSEN1, and NDUFS8; and Class VI is defined by DCAF17, cDNA FLJ75819, and SLC33A1. It is hypothesized that these classes represent biological mechanisms that may act alone or in any combination to manifest an Alzheimer’s pathology. Conclusions: Using a limited transcriptomic public database, six different classes that drive AD were uncovered, supporting the premise that AD is a heterogeneously complex disorder. The perspective classes highlighted genetic pathways associated with vasculogenesis, cellular signaling and differentiation, metabolic function, mitochondrial function, nitric oxide, and metal ion metabolism. The interplay among these genetic factors reveals a more profound underlying complexity of AD that may be responsible for the confluence of several biological factors. These results are not exhaustive; instead, they demonstrate that even within a relatively small study sample, next-generation machine intelligence can uncover multiple genetically driven subtypes. The models and the underlying hypotheses generated using novel analytic methods may translate into potential treatment pathways.
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Interactive platforms employing machine learning have defined distinct Alzheimer's disease subgroups characterized by pathways involving vasculogenesis and nitric oxide signaling.
“Interactive platforms employing machine learning have defined distinct AD subgroups characterized by pathways involving vasculogenesis and nitric oxide signaling344.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [344]
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Cheng, Y., Cawley, N. X., Yanık, T., Murthy, S. R. K., Liu, C., Kasikci, F., Abebe, D. T., & Loh, Y. P. (2016) · Translational Psychiatry · doi:10.1038/tp.2016.237 · Cited by 31 · see citing works
Lemire, B. D. (2017) · Mitochondrion · doi:10.1016/j.mito.2017.04.005 · Cited by 31 · see citing works
Galle, S. A., van der Spek, A., Drent, M. L., Brugts, M. P., Scherder, E., Janssen, J. A. M. J. L., Ikram, M. A., & van Duijn, C. M. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00020 · Cited by 31 · see citing works
Moll, L., & Schubert, M. (2012) · Current Gerontology and Geriatrics Research · doi:10.1155/2012/384094 · Cited by 30 · see citing works
Li, J.-Y., Chai, B., Zhang, W., Wu, X., Zhang, C., Fritze, D. M., Xia, Z., Patterson, C., & Mulholland, M. W. (2011) · BMC Neuroscience · doi:10.1186/1471-2202-12-95 · Cited by 28 · see citing works
Alzheimer’s Disease Genetics: Review of Novel Loci Associated with Disease
Tábuas-Pereira, M., Santana, I., Guerreiro, R., & Bras, J. (2020) · Current Genetic Medicine Reports · doi:10.1007/s40142-020-00182-y · Cited by 28 · see citing works
Tang, X., Li, Z., Zhang, W., & Yao, Z. (2019) · Nitric Oxide · doi:10.1016/j.niox.2019.07.006 · Cited by 27 · see citing works
Li, J., Kuick, R. D., Thompson, R. C., Misek, D. E., Lai, Y. ‐., Yun-Qiang, L. I. U., Chai, B.-X., Hanash, S., & Gantz, I. (2005) · Journal of Neuroendocrinology · doi:10.1111/j.1365-2826.2005.01317.x · Cited by 26 · see citing works
Zhang, X., Tan, F., Brovkovych, V. M., Zhang, Y., Lowry, J., & Skidgel, R. A. (2012) · Biological Chemistry · doi:10.1515/hsz-2012-0290 · Cited by 24 · see citing works
Quan, M., Zhao, T., Tang, Y., Luo, P., Wang, W., Qin, Q., Li, T., Wang, Q., Fang, J., & Jia, J. (2020) · Alzheimer s Research & Therapy · doi:10.1186/s13195-019-0572-2 · Cited by 23 · see citing works
Morello, G., & Cavallaro, S. (2015) · Future Medicinal Chemistry · doi:10.4155/fmc.15.60 · Cited by 20 · see citing works
Prominent Non-Memory Deficits in Alzheimer’s Disease Are Associated with Faster Disease Progression
Scheltens, N. M. E., Tijms, B. M., Heymans, M. W., Rabinovici, G. D., Cohn‐Sheehy, B. I., Miller, B. L., Kramer, J. H., Wolfsgruber, S., Wagner, M., Kornhuber, J., et al. (2018) · Journal of Alzheimer s Disease · doi:10.3233/jad-171088 · Cited by 20 · see citing works
Rahman, M. R., Islam, T., Zaman, T., Shahjaman, M., Karim, M. R., Huq, F., Quinn, J. M. W., Holsinger, R. M. D., & Moni, M. A. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/481879 · Cited by 19 · see citing works
The Effect of Signaling Latencies and Node Refractory States on the Dynamics of Networks
Silva, G. A. (2019) · Neural Computation · doi:10.1162/neco_a_01241 · Cited by 16 · see citing works
Multifaceted roles of ASB proteins and its pathological significance
Anasa, V. V., Ravanan, P., & Talwar, P. (2018) · Frontiers in Biology · doi:10.1007/s11515-018-1506-2 · Cited by 13 · see citing works
Proteolytic Processing of Neuregulin 2
Czarnek, M., & Bereta, J. (2019) · Molecular Neurobiology · doi:10.1007/s12035-019-01846-9 · Cited by 11 · see citing works
Li, H., Zeng, J., Huang, L., Wu, D., Liu, L., Liu, Y., & Yuan, Q. (2018) · Neuroscience Bulletin · doi:10.1007/s12264-018-0251-5 · Cited by 11 · see citing works
Dagan, H., Flashner-Abramson, E., Vasudevan, S., Jubran, M. R., Cohen, E., & Kravchenko‐Balasha, N. (2020) · Biomolecules · doi:10.3390/biom10040503 · Cited by 7 · see citing works
Identification of Neuregulin-2 as a novel stress granule component
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Next-Gen AI for Disease Definition, Patient Stratification, and Placebo EffectPreprint
Tsay, M. J., Geraci, J., & Agrawal, A. (2020) · doi:10.31219/osf.io/pc7ak · Cited by 4 · see citing works
Self-citationSYSTEMIC OVEREXPRESSION OF AT-1/SLC33A1 CAUSES A PROGERIA-LIKE PHENOTYPE
Peng, Y., Shapiro, S., Hewitt, K. J., Kong, G., Bresnick, E. H., Zhang, J., & Puglielli, L. (2017) · Innovation in Aging · doi:10.1093/geroni/igx004.1532 · Cited by 2 · see citing works
P.4.042 Gene expression profiling of Alzheimer's lymphocytes
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Flagged first, then by citations
2 of the 118 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Ren, F., Wei, J., Chen, Q., Hu, M., Yu, L., Mi, J., Zhou, X., Qin, D., Wu, J., & Wu, A. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.07.030 · Cited by 97 · see citing works
Alamro, H., Thafar, M. A., Albaradei, S., Gojobori, T., Essack, M., & Gao, X. (2023) · Scientific Reports · doi:10.1038/s41598-023-30904-5 · Cited by 60 · see citing works
Tang, A. S., Oskotsky, T., Havaldar, S., Mantyh, W. G., Bicak, M., Solsberg, C. W., Woldemariam, S., Zeng, B., Hu, Z., Oskotsky, B., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28273-0 · Cited by 45 · see citing works
A review and analysis of key biomarkers in Alzheimer’s disease
Zhang, Z., Liu, X., Zhang, S., Song, Z., Lu, K., & Yang, W. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1358998 · Cited by 20 · see citing works
Geraci, J., Bhargava, R., Qorri, B., Leonchyk, P., Cook, D. J., Cook, M., Sie, F., & Pani, L. (2024) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2023.1199736 · Cited by 18 · see citing works
Self-citationLi, M., & Larsen, P. A. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.12979 · Cited by 10 · see citing works
Choi, J., Bodenstein, D. F., Geraci, J., & Andreazza, A. C. (2021) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2021.08.011 · Cited by 10 · see citing works
Merritt, V. C., Zhang, R., Sherva, R., Ly, M. T., Marra, D. E., Panizzon, M. S., Tsuang, D. W., Hauger, R. L., The MVP Cognitive Decline and Dementia During Aging Working Group, V. M. V. P., & Logue, M. W. (2024) · Journal of Alzheimer s Disease · doi:10.1177/13872877241299130 · Cited by 8 · see citing works
Shattering cancer with quantum machine learning: A preview
Geraci, J. (2021) · Patterns · doi:10.1016/j.patter.2021.100281 · Cited by 7 · see citing works
Cook, M., Qorri, B., Amruth, B., Ziauddin, J., Pani, L., Yenkanchi, S., & Geraci, J. (2023) · Exploration of Medicine · doi:10.37349/emed.2023.00153 · Cited by 5 · see citing works
Self-citationGallucci, D., Ho, E. C. Y., Geraci, J., Loren, J., & Pani, L. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.945780 · Cited by 2 · see citing works
Classification of preclinical markers in Alzheimer's disease via WiSARD classifier
Gregorio, M. D., Costanzo, A. D., Motta, A., Paris, D., & Sorgente, A. (2022) · European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) · doi:10.14428/esann/2022.es2022-63 · Cited by 2 · see citing works
Cook, M., Qorri, B., Amruth, B., Ziauddin, J., Pani, L., Yenkanchi, S., & Geraci, J. (2021) · medRxiv · doi:10.1101/2021.07.27.21261075 · Cited by 2 · see citing works
Self-citation
- Authors
- Qorri, B., Tsay, M., Agrawal, A., Au, R., & Gracie, J.
- Affiliations
- Queen's University (CA)
- General Electric (Canada) (CA)
- GlaxoSmithKline (United States) (US)
- Boston University (US)
- Venue
- Exploration of Medicine
- Publisher
- Open Exploration Publishing
- Published
- 2020
- Volume
- 1
- Issue
- 6
- ISSN
- 2692-3106 (ISSN-L)
- OpenAlex
- W3111933166
- Cited by
- 13
- References
- 118
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 3 · 2022: 3 · 2021: 3
- FWCI
- 1.35
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Exploration of Medicine (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- OSF Preprints (OSF Preprints) (submittedVersion, cc-by)
- OSF Preprints (OSF Preprints) (submittedVersion)
- Topics
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- Alzheimer's disease, disease heterogeneity, Alzheimer's disease subtypes, transcriptomic data, postmortem brain, drug repurposing, augmented intelligence, TUBB1, PDE5A, presenilin 1, mitochondrial function, nitric oxide, metal ion metabolism, vasculogenesis, metabolic function
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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