August 4, 2025F1000ResearchOpen Access

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

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Novel deep learning algorithm enhances survival prediction for breast cancer using multi-omics data, suggesting improved clinical decisions.

The proposed deep learning model combines BiLSTM and CNN architectures for breast cancer survival prediction.
Analyzed multi-omics data from METABRIC (n=1980) and TCGA-BRCA (n=1080) datasets to evaluate model performance.
Achieved 98% accuracy on the METABRIC dataset, outperforming existing algorithms in AUC-ROC metrics.
Integration of MRMR feature selection improves interpretability and offers actionable insights for clinicians.

Nasarudin et al. (2025) studied this question.

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Novel deep learning algorithm enhances survival prediction for breast cancer using multi-omics data, suggesting improved clinical decisions.