Evidence map›Paper›PMID 41899421›Full record

ArticleCurrent issues in molecular biology2026

MVGAE: A Multi-View Graph Auto-Encoder Model for Drug Prediction of Non-Small Cell Lung Cancer Based on Synthetic Lethality.

Shaobo Hu, Runsheng Jiang, Ning Zhao

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Article in Current issues in molecular biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Shaobo HuCollege of Computer and Control Engineering, Northeast Forestry University, No. 26 Hexing Road, Harbin 150040, China.ORCID 0009-0009-0271-5337
Runsheng JiangCollege of Computer and Control Engineering, Northeast Forestry University, No. 26 Hexing Road, Harbin 150040, China.
Ning ZhaoCollege of Computer and Control Engineering, Northeast Forestry University, No. 26 Hexing Road, Harbin 150040, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identifying therapeutic target genes and their corresponding targeted drugs is of significant importance for the treatment of non-small cell lung cancer (NSCLC). This study proposes a multi-view graph auto-encoder model (MVGAE), which, together with the network-informed adaptive positive-unlabeled (NIAPU) and synthetic lethality multi-view graph auto-encoder (SLMGAE) model, constitutes an integrated computational framework. The framework integrates multi-source biological network data, including protein-protein interaction networks, disease-gene association information, and gene-drug bipartite graphs, for data mining. Through systematic analysis and computational screening, we ultimately predicted seven potential driver genes associated with NSCLC using the NIAPU model. The SLMGAE model predicted nine genes with synthetic lethality (SL) interactions to these driver genes as candidate therapeutic targets. Based on these SL targets, the MVGAE model further predicted corresponding targeted drugs. Notably, among the prioritized targets, existing studies indicate that

Indexed as

drug repurposingintegrated computational frameworknon-small cell lung cancersynthetic lethality

Identifiers

PMID41899421
PMCPMC13025638

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.