Article

Hybrid Stacking–Bagging Ensembles for Robust Multi-Omics Breast Cancer Prognosis

2026-01-23

Abstract excerpt

Accurate breast cancer risk prediction remains a central challenge in precision oncology due to the complexity and heterogeneity of underlying biological processes. While single-modality models based on clinical, gene expression, or copy number variation (CNV) data provide valuable prognostic insights, they often fail to capture complementary information across data sources. Conventional stacking ensembles improve...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: f6a68664-86aa-5240-b396-1c9af2f6ca68
DOI: 10.64898/2026.01.21.700935

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Hybrid Stacking–Bagging Ensembles for Robust Multi-Omics Breast Cancer PrognosisDOI 10.64898/2026.01.21.700935Read

COMBINE: A Novel Ensemble Learning Model for Improved Breast Cancer Prognosis in African American Women through Multi-Omics Data IntegrationSemantic proximity Read

COMBINE: A Comprehensive Multi-Omics Approach for Improving Breast Cancer Prognosis Classification in African American WomenSemantic proximity Read

A multimodal stacked ensemble model for breast cancer prognosis prediction: a proposed CNN hyperparameter optimization using the Tug of War (TWO) algorithmSemantic proximity Read

Increased Proportion of Variance Explained and Prediction Accuracy of Survival of Breast Cancer Patients with Use of Whole-Genome Multiomic ProfilesSemantic proximity Read

An Ensemble-Base Machine Learning Approach to Predict 2- and 10-Year Breast CancerSemantic proximity Read

Integrative Network Fusion: a multi-omics approach in molecular profilingSemantic proximity Read

Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis predictionSemantic proximity Read

Comparative analysis of statistical and deep learning-based multi-omics integration for breast cancer subtype classificationSemantic proximity Read

WIMOAD: Weighted Integration of Multi-Omics Data with Meta Learning for Alzheimer’s Disease DiagnosisSemantic proximity Read

Batch Normalization Followed by Merging Is Powerful for Phenotype Prediction Integrating Multiple Heterogeneous StudiesSemantic proximity Read

Consensus Through Diversity: A Comprehensive Benchmark of Multi-Omic Approaches for Precision Breast OncologySemantic proximity Read

MINT: A multivariate integrative method to identify reproducible molecular signatures across independent experiments and platformsSemantic proximity Read