ArticleScientific reports2021
Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- CausalCellInfer: Prioritizing Cell-Type-Specific Disease Associations From Genetically Predicted Transcriptomes in Biobank Cohorts.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- THe Biom: a platform for visualization and exploration of cancer transcriptomic biomarkers identified by robust feature selection.Bioinformatics advances · 2026Article
- Exploiting common patterns in diverse cancer types via multi-task learning.NPJ precision oncology · 2024Article
- Optimizing hybrid ensemble feature selection strategies for transcriptomic biomarker discovery in complex diseases.NAR genomics and bioinformatics · 2024Article
- A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction.Heliyon · 2024Article
- Breast cancer survival prognosis using the graph convolutional network with Choquet fuzzy integral.Scientific reports · 2023Article
- Development and validation of a prediction model for the diagnosis of breast cancer based on clinical and ultrasonic features.Gland surgery · 2023Article
- Learning from small medical data-robust semi-supervised cancer prognosis classifier with Bayesian variational autoencoder.Bioinformatics advances · 2023Article
- Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine.Computational and structural biotechnology journal · 2023Review
- OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features.Frontiers in neurology · 2023Article
- Identifying Drug Targets of Oral Squamous Cell Carcinoma through a Systems Biology Method and Genome-Wide Microarray Data for Drug Discovery by Deep Learning and Drug Design Specifications.International journal of molecular sciences · 2022Article
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3 authors.
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Abstract
Breast cancer is a heterogeneous disease. To guide proper treatment decisions for each patient, robust prognostic biomarkers, which allow reliable prognosis prediction, are necessary. Gene feature selection based on microarray data is an approach to discover potential biomarkers systematically. However, standard pure-statistical feature selection approaches often fail to incorporate prior biological knowledge and select genes that lack biological insights. Besides, due to the high dimensionality and low sample size properties of microarray data, selecting robust gene features is an intrinsically challenging problem. We hence combined systems biology feature selection with ensemble learning in this study, aiming to select genes with biological insights and robust prognostic predictive power. Moreover, to capture breast cancer's complex molecular processes, we adopted a multi-gene approach to predict the prognosis status using deep learning classifiers. We found that all ensemble approaches could improve feature selection robustness, wherein the hybrid ensemble approach led to the most robust result. Among all prognosis prediction models, the bimodal deep neural network (DNN) achieved the highest test performance, further verified by survival analysis. In summary, this study demonstrated the potential of combining ensemble learning and bimodal DNN in guiding precision medicine.
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