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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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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. |
format | Online Article Text |
id | pubmed-6412528 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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