---
title: "EG-BINN: Efficient Gated Neural Ensemble for Pan‑Tissue Epigenetic Age Prediction"
id: "biorxiv-6-eg-binn-parameter-efficient-ensembling-of-gated-biologically-informed-neural"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-6-eg-binn-parameter-efficient-ensembling-of-gated-biologically-informed-neural"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.10.17.759869v1?rss=1"
published_at: "2026-10-22T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# EG-BINN: Efficient Gated Neural Ensemble for Pan‑Tissue Epigenetic Age Prediction
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-6-eg-binn-parameter-efficient-ensembling-of-gated-biologically-informed-neural
- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.10.17.759869v1?rss=1)
- **Published At:** 2026-10-22T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The authors developed **EG-BINN**, a parameter-efficient ensemble of biologically informed neural networks that uses sample-specific gating to modulate representations across biologically annotated nodes. The source reported no peer review; this is a preprint. - Training data comprised 93 multi-tissue datasets; model evaluation used five-fold cross-validation and eight held-out Gene Expression Omnibus (GEO) datasets, per the preprint. - In five-fold cross-validation EG-BINN achieved the lowest mean and median absolute errors among matched linear and nonlinear baselines, according to the authors. The model also produced the lowest mean absolute error and root mean squared error across the held-out GEO datasets. - The gating mechanism produced pathway gate values that associated with age; the cohort-level trajectories varied by tissue type, with distinct patterns in blood, adipose tissue, skeletal muscle, and liver. - The authors applied EG-BINN across 11 TCGA cohorts. They reported significant tumor–normal differences in residual epigenetic age acceleration in six cohorts. - Explainability analyses included Shapley additive explanations (**SHAP**), which the study team used to identify recurrent pathway nodes contributing to tumor age predictions. - The preprint positions EG-BINN as combining pan-tissue age prediction accuracy with an explainable architecture for interrogating biologically labeled nodes across age, tissue, and disease contexts. - The source did not report specific model hyperparameters, exact error values, sample sizes per dataset, or training/dosing analogues; those details were not given in the abstract and must be obtained from the full text or supplementary materials for replication.
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Travyse Anthony Edwards University of Pennsylvania * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Travyse%2BAnthony%2BEdwards%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Edwards%20TA&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ATravyse%2BAnthony%2BEdwards%2B) * [ORCID record for Travyse Anthony Edwards](http://orcid.org/0000-0001-7273-2516 "Open in new tab") * For correspondence: travyse.edwards@gmail.com Qi Long University of Pennsylvania * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Qi%2BLong%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Long%20Q&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AQi%2BLong%2B) * [ORCID record for Qi Long](http://orcid.org/0000-0003-0660-5230 "Open in new tab") Li Shen University of Pennsylvania * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Li%2BShen%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Shen%20L&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ALi%2BShen%2B) * [ORCID record for Li Shen](http://orcid.org/0000-0002-5443-0503 "Open in new tab") * [Abstract](https://www.biorxiv.org/content/10.64898/2026.10.17.759869v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5894450/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.10.17.759869v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5894450/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.10.17.759869v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5894450/1) * [Supplementary material](https://www.biorxiv.org/content/10.64898/2026.10.17.759869v1.supplementary-material)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_data/node:5894450/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.10.17.759869v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5894450/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract Epigenetic aging clocks are widely used to evaluate biological aging, disease risk, and clinical interventions. However, most existing clocks rely on linear models that overlook nonlinear relationships between age and methylation patterns, or on deep learning architectures that are parameter-intensive and difficult to interpret. We developed EG-BINN, a parameter-efficient ensemble of biologically informed neural networks that uses sample-specific gating to modulate representations across biologically annotated nodes. Trained on 93 multi-tissue datasets, EG-BINN achieved the lowest mean and median absolute errors among matched linear and nonlinear baselines in five-fold cross-validation, as well as the lowest mean absolute and root mean squared errors across eight held-out Gene Expression Omnibus (GEO) datasets. Analysis of the gating mechanism revealed widespread associations between pathway gate values and age, including trajectories that varied across blood, adipose tissue, skeletal muscle, and liver. Across 11 TCGA cohorts, EG-BINN identified significant tumor--normal differences in residual epigenetic age acceleration in six cohorts, whereas Shapley additive explanations (SHAP) highlighted recurrent pathway nodes contributing to tumor age predictions. EG-BINN combines accurate pan-tissue age prediction with an explainable architecture for examining biologically labeled nodes across age, tissue, and disease contexts. ### Competing Interest Statement The authors have declared no competing interest. ## Funder Information Declared National Institutes of Health, https://ror.org/01cwqze88, T32 HG000046, U01 AG068057, U19 AG074879, R01 AG071470, U01 AG066833 Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a [CC-BY-NC-ND 4.0 International license](http://creativecommons.org/licenses/by-nc-nd/4.0/). bioRxiv and medRxiv thank the following for their generous financial support: > The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, The University of Edinburgh, University of Washington, and Vrije Universiteit Amsterdam. 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[ Download PDF](https://www.biorxiv.org/content/10.64898/2026.10.17.759869v1.full.pdf) Print/Save Options [Download PDF](https://www.biorxiv.org/content/biorxiv/early/2026/10/22/2026.10.17.759869.full.pdf)Full Text & In-line FiguresXML [More Info](https://www.biorxiv.org/about/FAQ#PrintOptions "More Information on Print/Save Options") [Supplementary Material ](https://www.biorxiv.org/content/10.64898/2026.10.17.759869v1.supplementary-material) [ Email](https://www.biorxiv.org/ "Email this Article") [ Share](https://www.biorxiv.org/) EG-BINN: Parameter-Efficient Ensembling of Gated Biologically Informed Neural Networks for Epigenetic Age Estimation Travyse Anthony Edwards, Qi Long, Li Shen bioRxiv 2026.10.17.759869; doi: https://doi.org/10.64898/2026.10.17.759869 This article is a preprint and has not been certified by peer review [[what does this mean?](https://www.biorxiv.org/about/FAQ#unrefereed)]. 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