Collaborative representation-based classification of microarray gene expression data

Shen, Lizhen; Jiang, Hua; He, Mingyi; Liu, Guoqing

doi:10.1371/journal.pone.0189533

Cited by 4 publications

(3 citation statements)

References 36 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method helps in reducing risks of overfitting. Reducing the dimensionality is a crucial method known as the collection of features and extraction of features [35,36].…”

Section: Dimension Reductionmentioning

confidence: 99%

Optimized hybrid investigative based dimensionality reduction methods for malaria vector using KNN classifier

Arowolo

Adebiyi

et al. 2021

J Big Data

View full text Add to dashboard Cite

RNA-Seq data are utilized for biological applications and decision making for the classification of genes. A lot of works in recent time are focused on reducing the dimension of RNA-Seq data. Dimensionality reduction approaches have been proposed in the transformation of these data. In this study, a novel optimized hybrid investigative approach is proposed. It combines an optimized genetic algorithm with Principal Component Analysis and Independent Component Analysis (GA-O-PCA and GAO-ICA), which are used to identify an optimum subset and latent correlated features, respectively. The classifier uses KNN on the reduced mosquito Anopheles gambiae dataset, to enhance the accuracy and scalability in the gene expression analysis. The proposed algorithm is used to fetch relevant features based on the high-dimensional input feature space. A fast algorithm for feature ranking is used to select relevant features. The performances of the model are evaluated and validated using the classification accuracy to compare existing approaches in the literature. The achieved experimental results prove to be promising for selecting relevant genes and classifying pertinent gene expression data analysis by indicating that the approach is capable of adding to prevailing machine learning methods.

show abstract

“…This method helps in reducing risks of overfitting. Reducing the dimensionality is a crucial method known as the collection of features and extraction of features [35,36].…”

Section: Dimension Reductionmentioning

confidence: 99%

Optimized hybrid investigative based dimensionality reduction methods for malaria vector using KNN classifier

Arowolo

Adebiyi

et al. 2021

J Big Data

View full text Add to dashboard Cite

show abstract

“…Dimensionality reduction methods are required, to remove redundancy and fetch irrelevant features that are disturbing the performance and operation by decreasing the feature ratios of the samples. This process helps in reducing the probability of overfitting [30]. Dimensionality reduction is two effective methods known as the feature selection [7] and feature extraction [31].…”

Section: Dimensionality Reductionmentioning

confidence: 99%

A Hybrid Heuristic Dimensionality Reduction Methods for Classifying Malaria Vector Gene Expression Data

Arowolo

Adebiyi

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Malaria is the world's leading cause of death, spread by Anopheles mosquitoes. Gene expression is a fundamental level where the effects of unseen vital revealing genes and developmental systems can be evident for detection of distinctions in malaria infections, to recognize the biological processes in human. Ribonucleic acid sequencing offers a large-scale measurable generated profiling transcriptional data results that help a variety of applications such as scientific and clinical condition studies. A fundamental limitation of ribonucleic acid sequencing consists of high dimensional, infrequent and noises, making classification of genes challenging. Several approaches have proposed enhancing the problem of the curse of dimensionality problem, requiring more improvement, yet it is critical to obtain accurate results. In this study, a hybrid dimensionality reduction technique proposes an optimized Genetic algorithm to pick pertinent subset features from the data. Features chosen is passed into principal component analysis and independent component analysis methods grounded on their class variants, to help transform the selected elements into a lower dimension separately. Support vector machine kernel classifiers used the reduced malaria vector dataset to assess the classification performance of the experiment.

show abstract

“…A Collaborative Representation (CR)-based classification with regularized least square was developed [31] to classify gene data. The CR codes a testing sample as a sparse linear combination of all training samples and then classifies the testing sample by evaluating which class leads to the minimum representation error.…”

Section: Introductionmentioning

confidence: 99%

Gene Microarray Cancer Classification using Correlation Based Feature Selection Algorithm and Rules Classifiers

Al-Batah¹,

Zaqaibeh²,

Alomari³

et al. 2019

Int. J. Onl. Eng.

View full text Add to dashboard Cite

Gene microarray classification problems are considered a challenge task since the datasets contain few number of samples with high number of genes (features). The genes subset selection in microarray data play an important role for minimizing the computational load and solving classification problems. In this paper, the Correlation-based Feature Selection (CFS) algorithm is utilized in the feature selection process to reduce the dimensionality of data and finding a set of discriminatory genes. Then, the Decision Table, JRip, and OneR are employed for classification process. The proposed approach of gene selection and classification is tested on 11 microarray datasets and the performances of the filtered datasets are compared with the original datasets. The experimental results showed that CFS can effectively screen irrelevant, redundant, and noisy features. In addition, the results for all datasets proved that the proposed approach with a small number of genes can achieve high prediction accuracy and fast computational speed. Considering the average accuracy for all the analysis of microarray data, the JRip achieved the best result as compared to Decision Table, and OneR classifier. The proposed approach has a remarkable impact on the classification accuracy especially when the data is complicated with multiple classes and high number of genes.

show abstract

Collaborative representation-based classification of microarray gene expression data

Cited by 4 publications

References 36 publications

Optimized hybrid investigative based dimensionality reduction methods for malaria vector using KNN classifier

Optimized hybrid investigative based dimensionality reduction methods for malaria vector using KNN classifier

A Hybrid Heuristic Dimensionality Reduction Methods for Classifying Malaria Vector Gene Expression Data

Gene Microarray Cancer Classification using Correlation Based Feature Selection Algorithm and Rules Classifiers

Contact Info

Product

Resources

About