(CDRGI)-Cancer detection through relevant genes identification
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
http://link.springer.com/content/pdf/10.1007/s00521-021-05739-8.pdf
Reference29 articles.
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2. Xiao Y, Wu J, Lin Z, Zhao X (2018) A semi-supervised deep learning method based on stacked sparse auto-encoder for cancer prediction using RNA-seq data. Comput Methods Progr Biomed 166:99–105
3. Elyasigomari V, Lee DA, Screen HR, Shaheed MH (2017) Development of a two-stage gene selection method that incorporates a novel hybrid approach using the cuckoo optimization algorithm and harmony search for cancer classification. J Biomed Inform 67:11–20
4. Khalifa NEM, Taha MHN, Ali DE, Slowik A, Hassanien AE (2020) Artificial intelligence technique for gene expression by tumor RNA-Seq data: a novel optimized deep learning approach. IEEE Access 8:22874–22883
5. Lu H, Chen J, Yan K, Jin Q, Xue Y, Gao Z (2017) A hybrid feature selection algorithm for gene expression data classification. Neurocomputing 256:56–62
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