ArticleNPJ precision oncology2022
Patient-level proteomic network prediction by explainable artificial intelligence.
Article in NPJ precision oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Oncolytic viruses: advanced strategies in cancer therapy.Signal transduction and targeted therapy · 2026Review
- Article
- Towards an interpretable deep learning model of cancer.NPJ precision oncology · 2025Review
- Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence.Nature cancer · 2025Article
- Convergence of evolving artificial intelligence and machine learning techniques in precision oncology.NPJ digital medicine · 2025Article
- [Explainable artificial intelligence in pathology].Pathologie (Heidelberg, Germany) · 2024Review
- Uncovering Protein Networks in Cardiovascular Proteomics.Molecular & cellular proteomics : MCP · 2023Review
- Single-cell gene regulatory network prediction by explainable AI.Nucleic acids research · 2023Article
- Imaging bridges pathology and radiology.Journal of pathology informatics · 2023Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Understanding the pathological properties of dysregulated protein networks in individual patients' tumors is the basis for precision therapy. Functional experiments are commonly used, but cover only parts of the oncogenic signaling networks, whereas methods that reconstruct networks from omics data usually only predict average network features across tumors. Here, we show that the explainable AI method layer-wise relevance propagation (LRP) can infer protein interaction networks for individual patients from proteomic profiling data. LRP reconstructs average and individual interaction networks with an AUC of 0.99 and 0.93, respectively, and outperforms state-of-the-art network prediction methods for individual tumors. Using data from The Cancer Proteome Atlas, we identify known and potentially novel oncogenic network features, among which some are cancer-type specific and show only minor variation among patients, while others are present across certain tumor types but differ among individual patients. Our approach may therefore support predictive diagnostics in precision oncology by inferring "patient-level" oncogenic mechanisms.
Identifiers
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Registered trials
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.