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Multimodal interpretable deep learning for transcriptome-informed precision oncology and drug mechanism analysis.

Code ↔ Paper

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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

Shell · 17 lines · 454 B · Apache-2.0

  1. #!/usr/bin/env bash
  3. set -euo pipefail
  5. ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
  6. cd "$ROOT"
  8. python ind_eval.py \
  9. --gpu 0 \
  10. --result_dir 'data/Example_train/results/' \
  11. --target_data_dir 'data/Example_eval' \
  12. --save_dir 'data/Example_eval/results/' \
  13. --batch_size 256 \
  14. --num_workers 4
  16. # If no GPU device is available, you can specify --cpu to force the model to run on the CPU.
  17. # specify --get_attn to get gene and pathway attention score.

Example_eval.sh at commit 532cca2, under Apache-2.0 · at the source

Overview

Authors: Ning Qu1,2, Xiaochu Tong1,2, Zhaokun Wang1,2, Panpan Shao1,3, Lehan Zhang1,2, Xiaoya Zhang1,2, Yuxin Xing1,2,4, Jin Liu1,5, Yitian Wang1,2, Sulin Zhang1,2, Mingyue Zheng1,2,4, Xutong Li1,2
  1. Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, China
  2. University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing, China
  3. School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, China
  4. School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China
  5. College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
Institutions: Chinese Academy of Sciences (China); Shanghai Institute of Materia Medica (China); University of Chinese Academy of Sciences (China); State Key Laboratory of Drug Research; Nanjing University of Chinese Medicine (China); Zhejiang University (China)
Journal: NPJ digital medicine, volume 9, issue 1, article 572
Dates: received 7 September 2025; accepted 30 April 2026; published online 13 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41746-026-02735-x · PMID 42129298 · PMCID PMC13396495 · OpenAlex W7160981008
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), other condition (population)
Methods: Machine learning, Statistics
Keywords: Cancer, Computational biology and bioinformatics, Drug discovery
Topic: Computational Drug Discovery Methods (Computational Theory and Mathematics, Computer Science), according to OpenAlex
Funding: Key Technologies R&D Program of Guangdong Province (2023B1111030004); Research Funds of Hangzhou Institute for Advanced Study (2025HIAS-ZL007); Innovative Drug Research and Development National Science and Technology Major Project (2025ZD1801200); Strategic Priority Research Program of the Chinese Academy of Sciences (XDB0830000, XDB1260301); National Natural Science Foundation of China (82204278); Lingang Laboratory (LGL-8888); Youth Innovation Promotion Association CAS (2023296); Research Funds of Hangzhou Institute for Advanced Study, UCAS (2025HIAS-ZL018); Shanghai Sailing Program (24YF2755600); Shanghai Municipal Science and Technology Major Project; China Postdoctoral Science Foundation (2024M763421)
Citations: not cited yet (Europe PMC); 73 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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myzhengSIMM/BioGDR

License: Apache-2.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 532cca204ccb024eebbde9d4f14df2b7bf843420, 16 April 2026
Languages: Python (23), Shell (8)
Size: 792 files, 31 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (env.yml)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: PyTorch (21 files), NumPy (7 files), pandas (4 files), scikit-learn (2 files), RDKit (1 file), SciPy (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
33 files
At the source: github.com/myzhengSIMM/BioGDR

Zenodo 15718571

License: Apache-2.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 2 files
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source: zenodo.org/records/15718571

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/10.1038/s41746-026-02735-x.

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;
  • 31 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.

Data availability statement

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

  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s41746-026-02735-x.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 3 keywords, 11 funders, 71 references.

Cite

This paper

Qu, N., Tong, X., Wang, Z., Shao, P., Zhang, L., Zhang, X., Xing, Y., Liu, J., Wang, Y., Zhang, S., Zheng, M., & Li, X. (2026). Multimodal interpretable deep learning for transcriptome-informed precision oncology and drug mechanism analysis. NPJ digital medicine, 9(1), 572. https://doi.org/10.1038/s41746-026-02735-x

BibTeX

@article{qu2026multimodal,
author = {Qu, Ning and Tong, Xiaochu and Wang, Zhaokun and Shao, Panpan and Zhang, Lehan and Zhang, Xiaoya and Xing, Yuxin and Liu, Jin and Wang, Yitian and Zhang, Sulin and Zheng, Mingyue and Li, Xutong},
title = {{Multimodal interpretable deep learning for transcriptome-informed precision oncology and drug mechanism analysis}},
journal = {NPJ digital medicine},
year = {2026},
month = may,
volume = {9},
number = {1},
pages = {572},
publisher = {Nature Publishing Group},
issn = {2398-6352},
doi = {10.1038/s41746-026-02735-x},
url = {https://doi.org/10.1038/s41746-026-02735-x},
pmid = {42129298},
pmcid = {PMC13396495}
}

RIS

TY - JOUR
AU - Qu, Ning
AU - Tong, Xiaochu
AU - Wang, Zhaokun
AU - Shao, Panpan
AU - Zhang, Lehan
AU - Zhang, Xiaoya
AU - Xing, Yuxin
AU - Liu, Jin
AU - Wang, Yitian
AU - Zhang, Sulin
AU - Zheng, Mingyue
AU - Li, Xutong
TI - Multimodal interpretable deep learning for transcriptome-informed precision oncology and drug mechanism analysis
T2 - NPJ digital medicine
J2 - NPJ Digit Med
PY - 2026
DA - 2026/05/13
VL - 9
IS - 1
SP - 572
SN - 2398-6352
PB - Nature Publishing Group
DO - 10.1038/s41746-026-02735-x
UR - https://doi.org/10.1038/s41746-026-02735-x
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Multimodal interpretable deep learning for transcriptome-informed precision oncology and drug mechanism analysis",
"container-title": "NPJ digital medicine",
"author": [
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{
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