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September 10, 2025F1000ResearchOpen Access

An Improved Deep Learning Algorithm for Breast Cancer Survival Prediction Based on Multi-Omics Data

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Authors

NNNurul Athirah NasarudinFAFatma Al‐JasmiNANor Hidayati Abdul Aziz

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Overview

Novel deep learning approach predicts breast cancer survival using multi-omics data, highlighting its clinical relevance.

Key Points

  • The proposed model achieves high accuracy, with 98% on METABRIC and 96% on TCGA datasets, marking a significant improvement.
  • Key performance metrics include AUC-ROC and accuracy, which were superior compared to existing prediction algorithms.
  • Using a combination of BiLSTM and CNN architectures, the model captures both temporal and spatial patterns effectively.
  • The integration of MRMR feature selection enhances the model's interpretability and accuracy in predicting breast cancer survival.

Cite This Study

Nasarudin et al. (2025) studied this question.

synapsesocial.com/papers/68c1925e9b7b07f3a061726fhttps://doi.org/10.12688/f1000research.166682.2
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Also Consider

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  1. 1An Improved Deep Learning Algorithm for Breast Cancer Survival Prediction Based on Multi-Omics Data2025 · 1 citations
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  5. 5A HYBRID EXPLAINABLE MULTI-OMICS MACHINE LEARNING FRAMEWORK FOR BREAST CANCER MUTATION PREDICTION AND CLINICAL RISK STRATIFICATION USING TCGA-BRCA DATA2026