The Unsupervised Feature Selection Algorithms Based on Standard Deviation and Cosine Similarity for Genomic Data Analysis

To tackle the challenges in genomic data analysis caused by their tens of thousands of dimensions while having a small number of examples and unbalanced examples between classes, the technique of unsupervised feature selection based on standard deviation and cosine similarity is proposed in this pap...

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Detalles Bibliográficos
Autores principales: Xie, Juanying, Wang, Mingzhao, Xu, Shengquan, Huang, Zhao, Grant, Philip W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Genetics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8155687/
https://www.ncbi.nlm.nih.gov/pubmed/34054930
http://dx.doi.org/10.3389/fgene.2021.684100

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8155687/
https://www.ncbi.nlm.nih.gov/pubmed/34054930
http://dx.doi.org/10.3389/fgene.2021.684100

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