OSCR

Integrative Multi-Omics and Machine Learning Analysis Identifies Therapeutic Targets and Drug Repurposing Candidates for Alzheimer's Disease.

Code ↔ Paper

The paper beside its authors' code: matches between them have not been computed for this paper yet.

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

Markdown · 107 lines · 5 KB · no license

  1. # Integrative Multi-Omics and Machine Learning Analysis Identifies Therapeutic Targets and Drug Repurposing Candidates for Alzheimer's Disease
  3. **Authors:** Bowen Xiao, Yong Q. Chen, Shaopeng Wang
  4. **Affiliation:** Jiangnan University, Wuxi 214122, China
  5. **Correspondence:** [email hidden]; [email hidden]
  6. **Journal:** *Biomedicines* (under review)
  8. ---
  10. ## Overview
  12. This repository provides processed data and result tables accompanying the manuscript. Raw transcriptomic datasets are publicly available from GEO (accession numbers GSE84422, GSE138852, GSE144136) and the Seattle Alzheimer's Disease Brain Cell Atlas (syn18681734).
  14. The analysis integrates:
  15. - Bulk RNA-seq from 1,047 samples (547 AD, 500 controls)
  16. - Single-cell RNA-seq from 48,481 prefrontal cortex nuclei
  17. - Three machine learning algorithms (LASSO, Random Forest, SVM)
  18. - Two-sample Mendelian randomization
  19. - Molecular docking of 2,634 FDA-approved drugs
  20. - 100 ns molecular dynamics simulations
  22. ---
  24. ## Repository Structure
  26. ```
  27. ├── data/ # Processed intermediate data (RDS format)
  28. │ ├── consensus_genes.rds # 12 consensus biomarker genes
  29. │ ├── candidate_hub_genes.rds # WGCNA hub gene candidates
  30. │ ├── mr_results_all.rds # Full MR results
  31. │ ├── pheno_data.rds # Sample phenotype table
  32. │ └── wgcna_module_colors.rds # WGCNA module color assignments
  33. │
  34. └── results/
  35. └── tables/ # Key result tables (CSV format)
  36. ├── DEG_results_full.csv # 2,847 differentially expressed genes
  37. ├── ML_performance_metrics.csv # Classifier AUC / accuracy metrics
  38. ├── individual_gene_AUC.csv # Per-gene ROC performance
  39. └── MR_forest_results.csv # Mendelian randomization causal estimates
  40. ```
  42. ---
  44. ## Key Results
  46. ### Machine Learning Classifier Performance
  48. | Method | AUC | Accuracy | Sensitivity | Specificity |
  49. |---------------|-------|----------|-------------|-------------|
  50. | LASSO | 0.955 | 0.899 | 0.925 | 0.872 |
  51. | Random Forest | 0.938 | 0.893 | 0.871 | 0.915 |
  52. | SVM | 0.963 | 0.914 | 0.903 | 0.926 |
  53. | Ensemble | 0.939 | 0.893 | 0.914 | 0.872 |
  55. ### Mendelian Randomization — Causal Targets
  57. | Gene | OR | 95% CI | P-value |
  58. |--------|------|-----------------|-----------|
  59. | APOE | 1.89 | 1.68 – 2.13 | 2.7×10⁻²⁶ |
  60. | TREM2 | 1.34 | 1.19 – 1.51 | 1.1×10⁻⁶ |
  61. | TYROBP | 1.28 | 1.12 – 1.47 | 4.2×10⁻⁴ |
  62. | CSF1R | 1.22 | 1.08 – 1.37 | 9.2×10⁻⁴ |
  63. | ITGAM | 1.19 | 1.06 – 1.34 | 3.7×10⁻³ |
  64. | CD68 | 1.17 | 1.04 – 1.32 | 8.9×10⁻³ |
  65. | CX3CR1 | 1.15 | 1.02 – 1.29 | 0.020 |
  66. | P2RY12 | 1.13 | 1.00 – 1.27 | 0.042 |
  68. ### Top Drug Repurposing Candidates
  70. | Drug–Target | Affinity (kcal/mol) | BBB Class |
  71. |----------------------|---------------------|-----------|
  72. | Curcumin–APOE | −10.3 | Medium |
  73. | Simvastatin–APOE | −9.5 | Low |
  74. | Indomethacin–TREM2 | −8.8 | High |
  75. | Celecoxib–CSF1R | −8.8 | High |
  76. | Resveratrol–TREM2 | −8.5 | Medium |
  77. | Memantine–CX3CR1 | −8.4 | High |
  78. | Donepezil–CD68 | −8.2 | High |
  79. | Quercetin–TYROBP | −7.8 | Medium |
  80. | Ibuprofen–ITGAM | −7.5 | High |
  81. | Aspirin–ITGAM | −7.2 | High |
  83. ---
  85. ## Data Sources
  87. | Dataset | Description | Access |
  88. |---------------|------------------------------------------|--------|
  89. | GSE84422 | Primary bulk RNA-seq (377 individuals) | [GEO](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84422) |
  90. | GSE138852 | Validation bulk RNA-seq (543 individuals)| [GEO](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE138852) |
  91. | GSE144136 | Validation bulk RNA-seq (127 individuals)| [GEO](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE144136) |
  92. | syn18681734 | Seattle AD Brain Cell Atlas (48,481 nuclei) | [Synapse](https://www.synapse.org/#!Synapse:syn18681734) |
  93. | IGAP GWAS | AD GWAS summary statistics | [IGAP](http://www.igap-alzheimer.org/) |
  94. | GTEx v8 | Brain cortex eQTL data | [GTEx](https://gtexportal.org/) |
  95. | DrugBank v5.1.8 | FDA-approved drug structures | [DrugBank](https://go.drugbank.com/) |
  97. ---
  99. ## Citation
  101. > Xiao B, Chen YQ, Wang S. Integrative Multi-Omics and Machine Learning Analysis Identifies Therapeutic Targets and Drug Repurposing Candidates for Alzheimer's Disease. *Biomedicines* 2026 (under review).
  103. ---
  105. ## License
  107. Data and results in this repository are released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Raw datasets are subject to their original repository terms of use.

README.md at commit 4df52f6, no license · at the source

Overview

Authors: Bowen Xiao1, Yong Q Chen1,2, Shaopeng Wang1
ORCID iDs: Bowen Xiao, Yong Q Chen
  1. Jiangnan University, Wuxi 214122, China
  2. School of Food Science and Technology, Jiangnan University, Wuxi 214122, China
Institutions: Jiangnan University (China)
Journal: Biomedicines, volume 14, issue 5, article 998
Dates: received 12 March 2026; accepted 20 April 2026; published online 27 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3390/biomedicines14050998 · PMID 42193325 · PMCID PMC13204273 · OpenAlex W7160120528
Open access: gold, a free copy (OpenAlex)
Status: empty repository
Categories: genetics / omics (modality), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Connectivity, Spectral & time-frequency
Keywords: Alzheimer’s disease, multi-omics, machine learning, drug repurposing, single-cell analysis, Mendelian randomization, molecular docking, WGCNA
Topic: Neuroinflammation and Neurodegeneration Mechanisms (Neurology, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 73 references in the paper

Abstract

Background/Objectives: Alzheimer’s disease (AD) remains a progressive neurodegenerative disorder with limited therapeutic options. This study aimed to develop an integrative multi-omics computational pipeline to identify diagnostic biomarkers and prioritize druggable therapeutic targets for AD. Methods: We integrated transcriptomic data from 1047 samples (547 AD, 500 controls) using weighted gene co-expression network analysis (WGCNA) and three machine learning algorithms (LASSO, Random Forest, SVM) with strict separation of training, feature selection, and evaluation. Single-cell RNA sequencing of 48,481 nuclei from entorhinal cortex, two-sample Mendelian randomization (MR) with Bayesian colocalization, and structure-based molecular docking with triplicate 500 ns molecular dynamics (MD) simulations were also employed. Results: Machine learning identified 10 consensus biomarker genes involved in synaptic vesicle cycling, ion transport, and calcium homeostasis (internal test AUC = 0.891, 95% CI: 0.836–0.946; external validation on GSE48350: AUC = 0.847, 95% CI: 0.798–0.896). Covariate-adjusted differential expression and MR with Bayesian colocalization converged on eight immune-related therapeutic targets including APOE, TREM2, and TYROBP (p<0.05; Bonferroni-corrected threshold p<0.00625). Single-cell analysis revealed oligodendrocyte expansion in AD (28.5% versus 24.8%), with target genes predominantly expressed in microglia and astrocytes. Virtual screening of 2634 FDA-approved drugs prioritized 10 exploratory repurposing candidates; indomethacin–TREM2 and celecoxib–CSF1R are primary exploratory candidates given structurally validated binding pockets. Triplicate MD simulations (15 μs aggregate) showed force-field-consistent structural stability (RMSD ≤ 3.2 Å). A quantitative multi-omics convergence framework identified four Tier 1 targets (APOE, TREM2, TYROBP, CX3CR1) supported by ≥5 analytical layers (Pperm=0.0003; note: three of five layers share the same transcriptomic input). Conclusions: These findings provide a multi-evidence computational framework linking diagnostic biomarkers and druggable neuroinflammatory targets for AD. All predictions require experimental validation in biochemical and cellular models before clinical conclusions can be drawn.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above.

alzheimer-analysis/multi-omics-ad

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 4df52f685825107b8ca42d36192ded75ceea860e, 12 March 2026
Size: 13 files, 0 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
1 file
At the source: github.com/alzheimer-analysis/multi-omics-ad

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 0 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data Availability Statement

The analysis code, configuration files, and Conda environment specification (environment.yml) are available at https://github.com/alzheimer-analysis/multi-omics-ad (tagged release v1.0; accessed on 19 April 2026). The repository includes a step-by-step pipeline guide (README.md), all key parameter settings, intermediate output files for each analytical module (WGCNA, machine learning, Mendelian randomization, molecular docking, and molecular dynamics), and exact software version specifications (R 4.2.0, Python 3.9, GROMACS 2023.1, AutoDock Vina 1.2.3) sufficient to reproduce all reported results from publicly available raw input data. Reviewer-level reproducibility queries may be directed to the corresponding authors. Raw datasets are available from GEO (GSE33000 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33000), GSE48350 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE48350), GSE138852 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE138852)), IGAP, and GTEx v8 as described in Section 2.1.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 8 keywords, 72 references.

Cite

This paper

Xiao, B., Chen, Y. Q., & Wang, S. (2026). Integrative Multi-Omics and Machine Learning Analysis Identifies Therapeutic Targets and Drug Repurposing Candidates for Alzheimer's Disease. Biomedicines, 14(5), 998. https://doi.org/10.3390/biomedicines14050998

BibTeX

@article{xiao2026integrative,
author = {Xiao, Bowen and Chen, Yong Q and Wang, Shaopeng},
title = {{Integrative Multi-Omics and Machine Learning Analysis Identifies Therapeutic Targets and Drug Repurposing Candidates for Alzheimer's Disease}},
journal = {Biomedicines},
year = {2026},
month = apr,
volume = {14},
number = {5},
pages = {998},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {2227-9059},
doi = {10.3390/biomedicines14050998},
url = {https://doi.org/10.3390/biomedicines14050998},
pmid = {42193325},
pmcid = {PMC13204273}
}

RIS

TY - JOUR
AU - Xiao, Bowen
AU - Chen, Yong Q
AU - Wang, Shaopeng
TI - Integrative Multi-Omics and Machine Learning Analysis Identifies Therapeutic Targets and Drug Repurposing Candidates for Alzheimer's Disease
T2 - Biomedicines
J2 - Biomedicines
PY - 2026
DA - 2026/04/27
VL - 14
IS - 5
SP - 998
SN - 2227-9059
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/biomedicines14050998
UR - https://doi.org/10.3390/biomedicines14050998
LA - en
ER -

CSL-JSON

{
"id": "10.3390/biomedicines14050998",
"type": "article-journal",
"title": "Integrative Multi-Omics and Machine Learning Analysis Identifies Therapeutic Targets and Drug Repurposing Candidates for Alzheimer's Disease",
"container-title": "Biomedicines",
"author": [
{
"family": "Xiao",
"given": "Bowen"
},
{
"family": "Chen",
"given": "Yong Q"
},
{
"family": "Wang",
"given": "Shaopeng"
}
],
"container-title-short": "Biomedicines",
"volume": "14",
"issue": "5",
"page": "998",
"DOI": "10.3390/biomedicines14050998",
"PMID": "42193325",
"PMCID": "PMC13204273",
"ISSN": "2227-9059",
"publisher": "Multidisciplinary Digital Publishing Institute (MDPI)",
"URL": "https://doi.org/10.3390/biomedicines14050998",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
27
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1002/alz.71804
A meta-refined human Alzheimer's disease-associated gene subset shows partial mouse-model pathway correspondence and limited cross-cohort machine-learning transportability.
Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association
In common: Alzheimer's / dementia, genetics / omics, cellular / molecular, 8 references
[2] doi:10.1002/alz.71823 [code]
Cellular transcriptomic signatures underpinning the heterogeneity of depression in Alzheimer's disease.
Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association
In common: Alzheimer's / dementia, genetics / omics, cellular / molecular, 7 references
[3] doi:10.1038/s41586-026-10793-0
Cell-type signatures of Alzheimer's disease shared across population groups.
Journal: Nature
In common: Alzheimer's / dementia, genetics / omics, cellular / molecular, 8 references
[4] doi:10.1093/braincomms/fcag326 [code]
Brain multiomic profiling identifies tau-related transcriptomic dysregulation in Alzheimer's disease.
Journal: Brain communications
In common: Alzheimer's / dementia, genetics / omics, cellular / molecular, 7 references
[5] doi:10.1038/s41419-026-08791-1
The PM20D1-OLE pathway induces microglia rewiring to ameliorate Alzheimer disease.
Journal: Cell death & disease
In common: NCBI GEO GSE33000, Alzheimer's / dementia, genetics / omics, cellular / molecular, 3 references
[6] doi:10.1038/s41467-026-70374-7 [code]
scTWAS: a powerful statistical framework for single-cell transcriptome-wide association studies.
Journal: Nature communications
In common: Alzheimer's / dementia, genetics / omics, cellular / molecular, 7 references
[7] doi:10.1126/sciadv.aed6825
SORLA up-regulation suppresses pathological effects in aged tauopathy mouse brain.
Journal: Science advances
In common: Alzheimer's / dementia, genetics / omics, cellular / molecular, 7 references
[8] doi:10.3390/ijms27146196
Investigation of the Potential Neuroprotective Mechanisms of &lt;i&gt;Acalypha indica&lt;/i&gt; Against Alzheimer's Disease by Integrated Bioinformatics Analysis.
Journal: International journal of molecular sciences
In common: NCBI GEO GSE33000, Alzheimer's / dementia, genetics / omics, cellular / molecular, 2 references
[9] doi:10.1371/journal.pcbi.1014074 [code]
Interpretable integration of unpaired multi-omics for Alzheimer's diagnosis via cross-modal transformer reconstruction.
Journal: PLoS computational biology
In common: NCBI GEO GSE33000, Alzheimer's / dementia, genetics / omics, 2 references
[10] doi:10.1186/s13195-026-02081-w [code]
Cellular state heterogeneity underlying sex differences in Alzheimer's disease based on single-cell transcriptome.
Journal: Alzheimer's research & therapy
In common: Alzheimer's / dementia, genetics / omics, cellular / molecular, 6 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.