Full text

Turn on search term navigation

© 2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Introduction With the emergence of deep sequencing technologies, considerable genomic data have become available. Since genomic data are usually high-dimension small-sample data, that is, the dimension of the gene is large, the dimension of the sample is small, and it is easy to cause interference when performing feature selection and difficult to understand the sample directly [1]. [...]to get more sparse effects, we combine embedding learning and sparse regression with L1 -norm through linear weighting to complete the corresponding feature selection and cluster analysis. [...]the size of the p-value is closely related to the relationship between the selected genes and the disease, and there is a negative correlation between them. [...]the level of ACC indicates the degree of excellence of the algorithm, and there is a positive correlation between them.

Details

Title
LJELSR: A Strengthened Version of JELSR for Feature Selection and Clustering
Author
Sha-Sha Wu; Mi-Xiao, Hou; Chun-Mei, Feng; Jin-Xing, Liu
Publication year
2019
Publication date
2019
Publisher
MDPI AG
ISSN
16616596
e-ISSN
14220067
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2332039617
Copyright
© 2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.