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LJELSR: A Strengthened Version of JELSR for Feature Selection and Clustering

Feature selection and sample clustering play an important role in bioinformatics. Traditional feature selection methods separate sparse regression and embedding learning. Later, to effectively identify the significant features of the genomic data, Joint Embedding Learning and Sparse Regression (JELS...

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Detalles Bibliográficos
Autores principales: Wu, Sha-Sha, Hou, Mi-Xiao, Feng, Chun-Mei, Liu, Jin-Xing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6412528/
https://www.ncbi.nlm.nih.gov/pubmed/30781701
http://dx.doi.org/10.3390/ijms20040886
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author Wu, Sha-Sha
Hou, Mi-Xiao
Feng, Chun-Mei
Liu, Jin-Xing
author_facet Wu, Sha-Sha
Hou, Mi-Xiao
Feng, Chun-Mei
Liu, Jin-Xing
author_sort Wu, Sha-Sha
collection PubMed
description Feature selection and sample clustering play an important role in bioinformatics. Traditional feature selection methods separate sparse regression and embedding learning. Later, to effectively identify the significant features of the genomic data, Joint Embedding Learning and Sparse Regression (JELSR) is proposed. However, since there are many redundancy and noise values in genomic data, the sparseness of this method is far from enough. In this paper, we propose a strengthened version of JELSR by adding the L(1)-norm constraint on the regularization term based on a previous model, and call it LJELSR, to further improve the sparseness of the method. Then, we provide a new iterative algorithm to obtain the convergence solution. The experimental results show that our method achieves a state-of-the-art level both in identifying differentially expressed genes and sample clustering on different genomic data compared to previous methods. Additionally, the selected differentially expressed genes may be of great value in medical research.
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spelling pubmed-64125282019-04-05 LJELSR: A Strengthened Version of JELSR for Feature Selection and Clustering Wu, Sha-Sha Hou, Mi-Xiao Feng, Chun-Mei Liu, Jin-Xing Int J Mol Sci Article Feature selection and sample clustering play an important role in bioinformatics. Traditional feature selection methods separate sparse regression and embedding learning. Later, to effectively identify the significant features of the genomic data, Joint Embedding Learning and Sparse Regression (JELSR) is proposed. However, since there are many redundancy and noise values in genomic data, the sparseness of this method is far from enough. In this paper, we propose a strengthened version of JELSR by adding the L(1)-norm constraint on the regularization term based on a previous model, and call it LJELSR, to further improve the sparseness of the method. Then, we provide a new iterative algorithm to obtain the convergence solution. The experimental results show that our method achieves a state-of-the-art level both in identifying differentially expressed genes and sample clustering on different genomic data compared to previous methods. Additionally, the selected differentially expressed genes may be of great value in medical research. MDPI 2019-02-18 /pmc/articles/PMC6412528/ /pubmed/30781701 http://dx.doi.org/10.3390/ijms20040886 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wu, Sha-Sha
Hou, Mi-Xiao
Feng, Chun-Mei
Liu, Jin-Xing
LJELSR: A Strengthened Version of JELSR for Feature Selection and Clustering
title LJELSR: A Strengthened Version of JELSR for Feature Selection and Clustering
title_full LJELSR: A Strengthened Version of JELSR for Feature Selection and Clustering
title_fullStr LJELSR: A Strengthened Version of JELSR for Feature Selection and Clustering
title_full_unstemmed LJELSR: A Strengthened Version of JELSR for Feature Selection and Clustering
title_short LJELSR: A Strengthened Version of JELSR for Feature Selection and Clustering
title_sort ljelsr: a strengthened version of jelsr for feature selection and clustering
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6412528/
https://www.ncbi.nlm.nih.gov/pubmed/30781701
http://dx.doi.org/10.3390/ijms20040886
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