ArticleFrontiers in genetics2019
deepDriver: Predicting Cancer Driver Genes Based on Somatic Mutations Using Deep Convolutional Neural Networks.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- A Novel Cancer Driver Genes Identification Method Based on Self-Supervised Dual Masked Graph Autoencoder.Interdisciplinary sciences, computational life sciences · 2026Article
- ONCOPLEX: an oncology-inspired hypergraph model integrating diverse biological knowledge for cancer driver gene prediction.Scientific reports · 2026Article
- Classification of driver and passenger mutations in different cancer types using deep neural networks.Bioinformatics advances · 2026Article
- Review
- Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning.Nature communications · 2025Article
- The theory of massively repeated evolution and full identifications of cancer-driving nucleotides (CDNs).eLife · 2024Article
- Multiview representation learning for identification of novel cancer genes and their causative biological mechanisms.Briefings in bioinformatics · 2024Article
- Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions.Cancer discovery · 2024Review
- Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes.Genome biology · 2024Article
- Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.Briefings in bioinformatics · 2024Review
- A review of genetic variant databases and machine learning tools for predicting the pathogenicity of breast cancer.Briefings in bioinformatics · 2023Review
- Systems Biology in Cancer Diagnosis Integrating Omics Technologies and Artificial Intelligence to Support Physician Decision Making.Journal of personalized medicine · 2023Review
- Network embedding framework for driver gene discovery by combining functional and structural information.BMC genomics · 2023Article
- Cancer driver mutations: predictions and reality.Trends in molecular medicine · 2023Review
- Identifying cancer driver genes based on multi-view heterogeneous graph convolutional network and self-attention mechanism.BMC bioinformatics · 2023Article
- Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction.Diagnostics (Basel, Switzerland) · 2023Review
- XMR: an explainable multimodal neural network for drug response prediction.Frontiers in bioinformatics · 2023Article
- BDL-SP: A Bio-inspired DL model for the identification of altered Signaling Pathways in Multiple Myeloma using WES data.American journal of cancer research · 2023Article
- Genes selection using deep learning and explainable artificial intelligence for chronic lymphocytic leukemia predicting the need and time to therapy.Frontiers in oncology · 2023Article
- Developing an Efficient Cancer Detection and Prediction Tool Using Convolution Neural Network Integrated with Neural Pattern Recognition.BioMed research international · 2023Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the advances in high-throughput technologies, millions of somatic mutations have been reported in the past decade. Identifying driver genes with oncogenic mutations from these data is a critical and challenging problem. Many computational methods have been proposed to predict driver genes. Among them, machine learning-based methods usually train a classifier with representations that concatenate various types of features extracted from different kinds of data. Although successful, simply concatenating different types of features may not be the best way to fuse these data. We notice that a few types of data characterize the similarities of genes, to better integrate them with other data and improve the accuracy of driver gene prediction, in this study, a deep learning-based method (deepDriver) is proposed by performing convolution on mutation-based features of genes and their neighbors in the similarity networks. The method allows the convolutional neural network to learn information within mutation data and similarity networks simultaneously, which enhances the prediction of driver genes. deepDriver achieves AUC scores of 0.984 and 0.976 on breast cancer and colorectal cancer, which are superior to the competing algorithms. Further evaluations of the top 10 predictions also demonstrate that deepDriver is valuable for predicting new driver genes.
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