An extensive multi-variant deep neural network approach to enhance genomic prediction of endometriosis

Zelia Soo, Lin Hua, Yue Yang, Mark Grosser, Mengjia Wu, Yanmin Zhang, Jie Lu
In: Neurocomputing · 2025 · vol. 656 , pp. 131496 · doi:10.1016/j.neucom.2025.131496 · W4414229965
OA: hybrid CC0
🔓 Open OA copy View on OpenAlex View at publisher
⚙ AI-generated summary by gemini-2.5-flash-lite, 2026-06-07 ⓘ

The EMV-DNN deep learning model integrates single nucleotide polymorphisms, indels, STRs, and CNVs to improve genomic prediction of endometriosis, outperforming conventional methods and revealing biologically relevant variant-gene-disease associations.

One-sentence paraphrase of the abstract; not a substitute for reading it. No clinical advice. How this works

Abstract

Deep neural networks have shown significant advancements in modelling complex non-linear relationships in high-dimensional biomedical data. Understanding the interplay between genetic variants and disease susceptibility is still a considerable challenge that prevents certain genomic diseases to be predicted accurately for clinical interventions. In this study, we introduce the Extensive Multi-Variant Deep Neural Network (EMV-DNN), an innovative deep learning methodology designed to enhance polygenic risk prediction. Unlike conventional polygenic risk score methods, EMV-DNN incorporates single nucleotide polymorphisms (SNPs) alongside structural variants including insertions and deletions (indels), short tandem repeats (STRs), and copy number variants (CNVs) using variant-specific subnetworks to extract informative embeddings which capture a richer and holistic genomic context. Evaluated on real-world cohorts from the UK Biobank and All of Us, EMV-DNN outperforms conventional PRS methods and classic machine learning algorithms across binary and multi-class prediction tasks. Beyond predictive performance, SHapley Additive exPlanations (SHAP) analysis revealed biologically plausible variant–gene–disease associations, highlighting pathways related to endometrial cell proliferation, fibrosis, and immune regulation. Our findings underscore the value of multi-variant integration and non-linear approaches to capture the intricate genetic architecture of complex genomic diseases. Despite challenges such as dataset limitations and the complexity of diseases with multiple contributing factors, the EMV-DNN methodology presents a promising avenue for enhancing the predictive accuracy of PRS, thereby facilitating personalized healthcare interventions and advancing our understanding of genetic predispositions to disease.

My notes (saved in your browser only)

Condition tags

endometriosis

Citation neighborhood (2-hop)

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. Outer rings show 2-hop neighbours — papers reached through the immediate citers/citees. [ collapse to 1-hop ]

References (63)

Cited by (1)

Source provenance

openalex
last seen: 2026-06-10T17:14:06.276822+00:00
License: CC0 · commercial use OK