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Deep contrastive learning framework identifies cell-type-specific drug targets in Alzheimer's disease.

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Paper

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

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The authors' code

Python · 77 lines · 3 KB · no license

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It can be read at the source: cl_ft_all_ppi_op_psol/check_results.py.

Overview

Authors: Yuxin Yang1,2, Jielin Xu1,2, Yuan Hou1,2, Yadi Zhou1,2, Andrew J Saykin3,4,5,6, Feixiong Cheng1,2,7
  1. Cleveland Clinic Genome Center, Cleveland Clinic Research, Cleveland Clinic, Cleveland, Ohio, USA
  2. Department of Genomic Sciences and Systems Biology, Cleveland Clinic Research, Cleveland Clinic, Cleveland, Ohio, USA
  3. Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine, Indianapolis, Indiana, USA
  4. Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA
  5. Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
  6. Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, USA
  7. Department of Molecular Medicine, Case Western Reserve University, Cleveland, Ohio, USA
Institutions: Cleveland Clinic (United States); Indiana University Indianapolis (United States); Indiana University School of Medicine; Case Western Reserve University (United States)
Journal: Alzheimer's & dementia (Amsterdam, Netherlands), volume 18, issue 3, article e70406
Dates: received 26 December 2025; accepted 2 June 2026; published online 29 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1002/dad2.70406 · PMID 42382038 · PMCID PMC13314553 · OpenAlex W7166591306
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Machine learning
Keywords: Alzheimer's disease, artificial intelligence, contrastive learning, deep learning, drug repurposing, protein–protein interaction
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Alzheimer's Association (ALZDISCOVERY-1051936); National Institute on Aging (R01 AG082118, U01AG073323, R01 AG092591, R01 AG076448, R33 AG083003, R01 AG092462, RF1 AG082211, R01 AG066707, R01AG084250); National Institute of Neurological Disorders and Stroke (RF1NS133812)
Citations: not cited yet (Europe PMC); 50 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

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yuxin212/CLARITY

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: f775abbd5ca8d1b8d294fd543fa2587d0c4b7532, 5 November 2025
Languages: Python (16), Shell (11)
Size: 40 files, 27 scripts
Software Heritage: not archived
Found in: “CODE AVAILABILITY”
Holds: README, environment (requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: PyTorch (14 files), scikit-learn (8 files), PyTorch Geometric (7 files), NumPy (5 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
28 files, not copied: shown from their source

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At the source: github.com/yuxin212/CLARITY

ChengF-Lab/CLARITY

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: f775abbd5ca8d1b8d294fd543fa2587d0c4b7532, 5 November 2025
Languages: Python (16), Shell (11)
Size: 40 files, 27 scripts
Software Heritage: not archived
Found in: “CODE AVAILABILITY”
Holds: README, environment (requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: PyTorch (14 files), scikit-learn (8 files), PyTorch Geometric (7 files), NumPy (5 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
28 files, not copied: shown from their source

OSCR keeps no copy of these files: this repository has no license that allows it. The reader above shows each one from its source, fetched by your browser at commit f775abb, when its fingerprint is the one OSCR verified. How this works.

At the source: github.com/ChengF-Lab/CLARITY

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1002/dad2.70406.

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 54 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

No dataset and no data link were found in the paper.

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1002/dad2.70406.

Versions

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Version 2, 28 September 2026

  • Funding: added Alzheimer's Association: ALZDISCOVERY-1051936; National Institute on Aging: R01 AG082118, U01AG073323, R01 AG092591, R01 AG076448, R33 AG083003, R01 AG092462, RF1 AG082211, R01 AG066707, R01AG084250; National Institute of Neurological Disorders and Stroke: RF1NS133812

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 6 keywords, 50 references.

Cite

This paper

Yang, Y., Xu, J., Hou, Y., Zhou, Y., Saykin, A. J., & Cheng, F. (2026). Deep contrastive learning framework identifies cell-type-specific drug targets in Alzheimer's disease. Alzheimer's & dementia (Amsterdam, Netherlands), 18(3), e70406. https://doi.org/10.1002/dad2.70406

BibTeX

@article{yang2026deep,
author = {Yang, Yuxin and Xu, Jielin and Hou, Yuan and Zhou, Yadi and Saykin, Andrew J and Cheng, Feixiong},
title = {{Deep contrastive learning framework identifies cell-type-specific drug targets in Alzheimer's disease}},
journal = {Alzheimer's \& dementia (Amsterdam, Netherlands)},
year = {2026},
month = jun,
volume = {18},
number = {3},
pages = {e70406},
publisher = {Wiley},
issn = {2352-8729},
doi = {10.1002/dad2.70406},
url = {https://doi.org/10.1002/dad2.70406},
pmid = {42382038},
pmcid = {PMC13314553}
}

RIS

TY - JOUR
AU - Yang, Yuxin
AU - Xu, Jielin
AU - Hou, Yuan
AU - Zhou, Yadi
AU - Saykin, Andrew J
AU - Cheng, Feixiong
TI - Deep contrastive learning framework identifies cell-type-specific drug targets in Alzheimer's disease
T2 - Alzheimer's & dementia (Amsterdam, Netherlands)
J2 - Alzheimers Dement (Amst)
PY - 2026
DA - 2026/06/29
VL - 18
IS - 3
SP - e70406
SN - 2352-8729
PB - Wiley
DO - 10.1002/dad2.70406
UR - https://doi.org/10.1002/dad2.70406
LA - en
ER -

CSL-JSON

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"id": "10.1002/dad2.70406",
"type": "article-journal",
"title": "Deep contrastive learning framework identifies cell-type-specific drug targets in Alzheimer's disease",
"container-title": "Alzheimer's & dementia (Amsterdam, Netherlands)",
"author": [
{
"family": "Yang",
"given": "Yuxin"
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"container-title-short": "Alzheimers Dement (Amst)",
"volume": "18",
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"DOI": "10.1002/dad2.70406",
"PMID": "42382038",
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"ISSN": "2352-8729",
"publisher": "Wiley",
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"language": "en",
"issued": {
"date-parts": [
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