Open Access
| Issue |
BIO Web Conf.
Volume 163, 2025
2025 15th International Conference on Bioscience, Biochemistry and Bioinformatics (ICBBB 2025)
|
|
|---|---|---|
| Article Number | 01003 | |
| Number of page(s) | 10 | |
| Section | Bioinformatics and Computational Biology | |
| DOI | https://doi.org/10.1051/bioconf/202516301003 | |
| Published online | 06 March 2025 | |
- Schulte-Sasse, Roman, et al. “Integration of multiomics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms.” Nature Machine Intelligence 3.6 (2021): 513–526. [CrossRef] [Google Scholar]
- Chatzianastasis, Michail, Michalis Vazirgiannis, and Zijun Zhang. “Explainable multilayer graph neural network for cancer gene prediction.” Bioinformatics 39.11 (2023): btad643. [CrossRef] [PubMed] [Google Scholar]
- Gorlov, Ivan P., et al. “Gene characteristics predicting missense, nonsense and frameshift mutations in tumor samples.” BMC bioinformatics 19 (2018): 1–14. [CrossRef] [PubMed] [Google Scholar]
- Wang, Tongxin, et al. “Moronet: multi-omics integration via graph convolutional networks for biomedical data classification.” BioRxiv (2020): 2020–07. [Google Scholar]
- Jreidy, Maria Al, et al. “Graph Convolution Networks for Unsupervised Learning.” International Conference on Smart Applications and Data Analysis. Cham: Springer International Publishing, 2022. [Google Scholar]
- Jung, Seunghwan, Seunghyun Wang, and Doheon Lee. “CancerGATE: Prediction of cancer-driver genes using graph attention autoencoders.” Computers in Biology and Medicine 176 (2024): 108568. [CrossRef] [PubMed] [Google Scholar]
- Frisoni, Giacomo, et al. “Unsupervised event graph representation and similarity learning on biomedical literature.” Sensors 22.1 (2021): 3. [CrossRef] [PubMed] [Google Scholar]
- Bica, Ioana, et al. “Unsupervised generative and graph representation learning for modelling cell differentiation.” Scientific reports 10.1 (2020): 9790. [CrossRef] [PubMed] [Google Scholar]
- Liu, Can, et al. “A classification method of gastric cancer subtype based on residual graph convolution network.” Frontiers in Genetics 13 (2023): 1090394. [CrossRef] [PubMed] [Google Scholar]
- Singh, Vijay Pal, Aijaz Ahmad, and Kushal Manoharrao Jagtap. “Weighted shapley value: A cooperative game theory for loss allocation in distribution systems.” Frontiers in Energy Research 11 (2023): 1129846. [CrossRef] [Google Scholar]
- Lundstrom, Daniel D., Tianjian Huang, and Meisam Razaviyayn. “A rigorous study of integrated gradients method and extensions to internal neuron attributions.” International Conference on Machine Learning. PMLR, 2022. [Google Scholar]
- Chang, Heng, et al. “Path-based Explanation for Knowledge Graph Completion.” Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2024. [Google Scholar]
- He, Mingguo, Zhewei Wei, and Ji-Rong Wen. “Convolutional neural networks on graphs with chebyshev approximation, revisited.” Advances in neural information processing systems 35 (2022): 7264–7276. [Google Scholar]
- Subramanian, Indhupriya, et al. “Multi-omics data integration, interpretation, and its application.” Bioinformatics and biology insights 14 (2020): 1177932219899051. [CrossRef] [Google Scholar]
- Wang, Tao, et al. “A graph-based interpretability method for deep neural networks.” Neurocomputing 555 (2023): 126651. [CrossRef] [Google Scholar]
- Leiserson, M. D. et al. Pan-cancer network analysis identifes combinations of rare somatic mutations across pathways and protein complexes. Nat. Genet. 47, 106–114 (2015). [CrossRef] [PubMed] [Google Scholar]
- Mermel, C. H. et al. GISTIC2.0 facilitates sensitive and confdent localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12, R41 (2011). [CrossRef] [PubMed] [Google Scholar]
- Johnson, W. E., Li, C. Rabinovic, A. Adjusting batch efects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007). [CrossRef] [PubMed] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.