ArticleJournal of personalized medicine2022
Leveraging Deep Learning Techniques and Integrated Omics Data for Tailored Treatment of Breast Cancer.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Comprehensive applications of the artificial intelligence technology in new drug research and development.Health information science and systems · 2024Review
- An Innovative Multi-Omics Model Integrating Latent Alignment and Attention Mechanism for Drug Response Prediction.Journal of personalized medicine · 2024Article
- Classifying breast cancer subtypes on multi-omics data via sparse canonical correlation analysis and deep learning.BMC bioinformatics · 2024Article
- Three-Dimensional Postoperative Results Prediction for Orthognathic Surgery through Deep Learning-Based Alignment Network.Journal of personalized medicine · 2022Article
- A survey on multi-omics-based cancer diagnosis using machine learning with the potential application in gastrointestinal cancer.Frontiers in medicine · 2022Review
- Artificial intelligence assists precision medicine in cancer treatment.Frontiers in oncology · 2022Review
- Multiclass Cancer Prediction Based on Copy Number Variation Using Deep Learning.Computational intelligence and neuroscience · 2022Article
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Authors and funding
2 authors.
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Abstract
Multiomics data of cancer patients and cell lines, in synergy with deep learning techniques, have aided in unravelling predictive problems related to cancer research and treatment. However, there is still room for improvement in the performance of the existing models based on the aforementioned combination. In this work, we propose two models that complement the treatment of breast cancer patients. First, we discuss our deep learning-based model for breast cancer subtype classification. Second, we propose DCNN-DR, a deep convolute.ion neural network-drug response method for predicting the effectiveness of drugs on in vitro and in vivo breast cancer datasets. Finally, we applied DCNN-DR for predicting effective drugs for the basal-like breast cancer subtype and validated the results with the information available in the literature. The models proposed use late integration methods and have fairly better predictive performance compared to the existing methods. We use the Pearson correlation coefficient and accuracy as the performance measures for the regression and classification models, respectively.
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