Improving PLS–RFE based gene selection for microarray data classification

Wang, Aiguo; An, Ning; Chen, Guilin; Li, Lian; Alterovitz, Gil

doi:10.1016/j.compbiomed.2015.04.011

Cited by 29 publications

(3 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [ 42 ], informative genes were selected using mutual information between genes and classes, and the disease was classified using selected genes and SVM. Integration of the partial least squares (PLS) based recursive feature elimination with simulated annealing and square root was produced in [ 43 ] and employed for gene selection.…”

Section: Previous Workmentioning

confidence: 99%

Gene selection using pyramid gravitational search algorithm

et al. 2022

View full text Add to dashboard Cite

Genetics play a prominent role in the development and progression of malignant neoplasms. Identification of the relevant genes is a high-dimensional data processing problem. Pyramid gravitational search algorithm (PGSA), a hybrid method in which the number of genes is cyclically reduced is proposed to conquer the curse of dimensionality. PGSA consists of two elements, a filter and a wrapper method (inspired by the gravitational search algorithm) which iterates through cycles. The genes selected in each cycle are passed on to the subsequent cycles to further reduce the dimension. PGSA tries to maximize the classification accuracy using the most informative genes while reducing the number of genes. Results are reported on a multi-class microarray gene expression dataset for breast cancer. Several feature selection algorithms have been implemented to have a fair comparison. The PGSA ranked first in terms of accuracy (84.5%) with 73 genes. To check if the selected genes are meaningful in terms of patient’s survival and response to therapy, protein-protein interaction network analysis has been applied on the genes. An interesting pattern was emerged when examining the genetic network. HSP90AA1, PTK2 and SRC genes were amongst the top-rated bottleneck genes, and DNA damage, cell adhesion and migration pathways are highly enriched in the network.

show abstract

Section: Previous Workmentioning

confidence: 99%

Gene selection using pyramid gravitational search algorithm

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Furthermore, Xi, Gu, Baniasadi & Raftery (2014) discussed the PLS-DA with applications to metabolites data. Other articles involving the usage of PLS include: Dong, Zhang, Zhu, Wang & Wang (2014) who used PLS to investigate the underlying mechanism of the post-traumatic stress disorder (PTSD) using microarray data; Gusnanto, Ploner, Shuweihdi & Pawitan 2013, who made gene selection based on partial least squares and logistic regression random-effects (RE) in classification models; gene selection involving PLS was also done by Wang, An, Chen, Li & Alterovitz (2015). The sparse PLS has also been utilized by many researchers; for instance, Chun & Keles (2009),Lee, Lee, Lee & Pawitan (2011) and Chung & Keles (2010) provided an efficient algorithm for the implementation of sparse PLS for variable selection in high dimensional data.…”

Section: Partial Least Squares (Pls) and Some Of Its Applications In mentioning

confidence: 99%

PLS Generalized Linear Regression and Kernel Multilogit Algorithm (KMA) for Microarray Data Classiﬁcation Problem

Wagala¹,

González–Farías

Ramos

et al. 2020

Rev. colomb. estad.

View full text Add to dashboard Cite

This study involves the implentation of the extensions of the partial least squares generalized linear regression (PLSGLR) by combining it with logistic regression and linear discriminant analysis, to get a partial least squares generalized linear regression-logistic regression model (PLSGLR-log), and a partial least squares generalized linear regression-linear discriminant analysis model (PLSGLRDA). A comparative study of the obtained classiﬁers with the classical methodologies like the k-nearest neighbours (KNN), linear discriminant analysis (LDA), partial least squares discriminant analysis (PLSDA), ridge partial least squares (RPLS), and support vector machines(SVM) is then carried out. Furthermore, a new methodology known as kernel multilogit algorithm (KMA) is also implemented and its performance compared with those of the other classiﬁers. The KMA emerged as the best classiﬁer based on the lowest classiﬁcation error rates compared to the others when applied to the types of data are considered; the un- preprocessed and preprocessed.

show abstract

“…Furthermore,Xi et al (2014) discussed the PLS-DA with applications to metabolites data. Other articles involving the usage of PLS include:Dong et al (2014) who used PLS to investigate the underlying mechanism of the post-traumatic stress disorder (PTSD) using microarray data;Gusnanto et al (2013), who made gene selection based on partial least squares and logistic regression random-effects (RE) in classification models; gene selection involving PLS was also done byWang et al (2015). The sparse PLS has also been utilized by many researchers; for instance,Chun and Keles (2009);Lee et al (2011) and ?…”

mentioning

confidence: 99%

PLS Generalized Linear Regression and Kernel Multilogit Algorithm (KMA) for Microarray Data Classification

Wagala¹,

González–Farías²,

Ramos³

et al. 2019

Preprint

View full text Add to dashboard Cite

We implement extensions of the partial least squares generalized linear regression (PLSGLR)due to Bastien et al. (2005) through its combination with logistic regression and linear discriminant analysis, to get a partial least squares generalized linear regression-logistic regression model (PLSGLR-log), and a partial least squares generalized linear regression-linear discriminant analysis model (PLSGLRDA). These two classification methods are then compared with classical methodologies like the k-nearest neighbours (KNN), linear discriminant analysis (LDA), partial least squares discriminant analysis (PLSDA), ridge partial least squares (RPLS), and support vector machines(SVM). Furthermore, we implement the kernel multilogit algorithm (KMA) by Dalmau et al. (2015) and compare its performance with that of the other classifiers. The results indicate that for both un-preprocessed and preprocessed data, the KMA has the lowest classification error rates.

show abstract

Improving PLS–RFE based gene selection for microarray data classification

Cited by 29 publications

References 43 publications

Gene selection using pyramid gravitational search algorithm

Gene selection using pyramid gravitational search algorithm

PLS Generalized Linear Regression and Kernel Multilogit Algorithm (KMA) for Microarray Data Classiﬁcation Problem

PLS Generalized Linear Regression and Kernel Multilogit Algorithm (KMA) for Microarray Data Classification

Contact Info

Product

Resources

About