---
title: "DAG-HEART: DAG-Guided Transfer Learning to Improve Equity-Aware Breast Cancer Prediction"
id: "biorxiv-0-dag-heart-directed-acyclic-graph-guided-health-equity-aware-representation"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-0-dag-heart-directed-acyclic-graph-guided-health-equity-aware-representation"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.08.31.748384v1?rss=1"
published_at: "2026-09-03T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# DAG-HEART: DAG-Guided Transfer Learning to Improve Equity-Aware Breast Cancer Prediction
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-0-dag-heart-directed-acyclic-graph-guided-health-equity-aware-representation
- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.08.31.748384v1?rss=1)
- **Published At:** 2026-09-03T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The authors present **DAG-HEART**, a directed acyclic graph–guided multi-omics representation and transfer-learning framework designed to address outcome prediction for underrepresented groups in breast cancer datasets. - DAG-HEART extends a prior transfer-learning approach with data augmentation and imposes biologically informed directional constraints when integrating multi-omics modalities. - The framework uses TCGA-BRCA mRNA, miRNA, and DNA-methylation data as input for model development and evaluation. - Evaluation focused on prediction of **progression-free interval** for a data-minority group, reflecting attention to health equity and demographic imbalance in genomic cohorts. - DAG-guided nonlinear integration consistently improved predictive performance relative to direction-agnostic and correlation-based representations, according to the authors' results reported in the source. - Imposing biologically motivated directional constraints generally outperformed reversed or unconstrained directed structures in the reported comparisons. - Recurrently selected features converged on **extracellular-matrix** and regulatory pathways, supporting clinically meaningful risk stratification in the authors' analysis. - The authors emphasize interpretability: DAG-HEART combines directed multi-omics structure with transfer learning to offer an interpretable strategy under data imbalance across racial groups. - The work is presented as a preprint (bioRxiv) and has not been peer reviewed; competing interests were declared as none. - Funding sources reported include the U.S. National Science Foundation, National Cancer Institute (NCI), National Institute on Alcohol Abuse and Alcoholism (NIAAA), NIH Office of the Director, Pediatric Cancer Research Group, and Nebraska Research Initiative. - Specific implementation details, quantitative performance metrics, and full methodological parameters were not reported in the source summary and would require consultation of the full preprint for complete technical reproducibility.
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Minjeong Baek University of Nebraska Medical Center * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Minjeong%2BBaek%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Baek%20M&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AMinjeong%2BBaek%2B) * [ORCID record for Minjeong Baek](http://orcid.org/0009-0006-9836-011X "Open in new tab") Jieqiong Wang University of Nebraska Medical Center * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Jieqiong%2BWang%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Wang%20J&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AJieqiong%2BWang%2B) * [ORCID record for Jieqiong Wang](http://orcid.org/0009-0009-2040-9552 "Open in new tab") Shibiao Wan University of Nebraska Medical Center * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Shibiao%2BWan%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Wan%20S&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AShibiao%2BWan%2B) * [ORCID record for Shibiao Wan](http://orcid.org/0000-0003-0661-2684 "Open in new tab") * For correspondence: swan@unmc.edu * [Abstract](https://www.biorxiv.org/content/10.64898/2026.08.31.748384v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5751930/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.08.31.748384v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5751930/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.08.31.748384v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5751930/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.08.31.748384v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5751930/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract Breast cancer outcome prediction remains challenging for underrepresented populations because genomic datasets are demographically imbalanced and conventional multi-omics integration largely relies on undirected molecular similarity. We developed DAG-HEART, a directed acyclic graph-guided multi-omics transfer-learning framework that extends our previous transfer learning strategy with data augmentation. Using TCGA-BRCA mRNA, miRNA, and DNA-methylation data, DAG-HEART was evaluated for progression-free interval prediction in a data-minority group. DAG-guided nonlinear integration consistently improved predictive performance relative to direction-agnostic and correlation-based representations, while biologically motivated directional constraints generally outperformed reversed or unconstrained structures. Recurrently selected features converged on extracellular-matrix and regulatory pathways and supported clinically meaningful risk stratification. DAG-HEART provides an interpretable strategy for combining directed multi-omics structure with transfer learning under data imbalance across racial groups. ### Competing Interest Statement The authors have declared no competing interest. ## Funder Information Declared U.S. National Science Foundation, https://ror.org/021nxhr62, 2500836, 2614824 National Cancer Institute of the National Institutes of Health, R03CA317707 National Institute Of Alcohol Abuse And Alcoholism of the National Institutes of Health, R21AA032098 the Office Of The Director, National Institutes Of Health of the National Institutes of Health, R03OD038391 Pediatric Cancer Research Group, part of the Child Health Research Institute Nebraska Research Initiative (NRI) 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/10.64898/2026.08.31.748384v1.full.pdf) Print/Save Options [Download PDF](https://www.biorxiv.org/content/biorxiv/early/2026/09/03/2026.08.31.748384.full.pdf)Full Text & In-line FiguresXML [More Info](https://www.biorxiv.org/about/FAQ#PrintOptions "More Information on Print/Save Options") [ Email](https://www.biorxiv.org/ "Email this Article") [ Share](https://www.biorxiv.org/) DAG-HEART: Directed Acyclic Graph-Guided Health Equity-Aware Representation Transfer Learning Framework for Breast Cancer Minjeong Baek, Jieqiong Wang, Shibiao Wan bioRxiv 2026.08.31.748384; doi: https://doi.org/10.64898/2026.08.31.748384 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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