Applying SCALEX scRNA-Seq Data Integration for Precise Alzheimer’s Disease Biomarker Discovery
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-34171-7_23
Reference20 articles.
1. Jiang, J., Cankun, W., Ren, Q., Hongjun, F., Qin, M.: ScREAD: a single-cell RNA-Seq database for alzheimer’s disease. IScience 23(11), 101769 (20 Nov 2020)
2. Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowedge Discovery and Data Mining (KDD ‘16). Association for Computing Machinery, New York,pp. 785–794 (2016). https://doi.org/10.1145/2939672.2939785
3. Lazaros, K., Tasoulis, S., Vrahatis, A., Plagianakos, V.: Feature selection for high dimensional data using supervised machine learning techniques. In: 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, pp. 3891–3894 (2022). https://doi.org/10.1109/BigData55660.2022.10020654
4. Mathys, H., Davila-Velderrain, J., Peng, Z., et al.: Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019). https://doi.org/10.1038/s41586-019-1195-2
5. Paplomatas, P., Krokidis, M.G., Vlamos, P., Vrahatis, A.G.: An ensemble feature selection approach for analysis and modeling of transcriptome data in alzheimer’s disease. Appl. Sci. 13, 2353 (2023). https://doi.org/10.3390/app13042353
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