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

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Micheal Olaolu Arowolo ◽  
Marion Olubunmi Adebiyi ◽  
Ayodele Ariyo Adebiyi ◽  
Oludayo Olugbara
Keyword(s):  
Gene Expression ◽  
Dimensionality Reduction ◽  
Principal Component ◽  
Feature Space ◽  
Component Analysis ◽  
Rna Seq ◽  
Knn Classifier ◽  
Data Dimensionality Reduction ◽  
Reduction Methods ◽  
Mosquito Anopheles Gambiae

AbstractRNA-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.

scholarly journals Optimized Hybrid Heuristic Based Dimensionality Reduction Methods for Malaria Vector Using KNN Classifier

2020 ◽  
Author(s):  
Micheal Olaolu Arowolo ◽  
Marion Olubunmi Adebiyi ◽  
Ayodele Ariyo Adebiyi ◽  
Oludayo Olugbara
Keyword(s):  
Gene Expression ◽  
Dimensionality Reduction ◽  
Principal Component ◽  
Feature Space ◽  
Component Analysis ◽  
Rna Seq ◽  
Knn Classifier ◽  
Data Dimensionality Reduction ◽  
Reduction Methods ◽  
Mosquito Anopheles Gambiae

Abstract 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 a capable addition to prevailing machine learning methods.

scholarly journals Enhanced Dimensionality Reduction Methods for Classifying Malaria Vector Dataset using Decision Tree

2021 ◽  
Vol 50 (9) ◽  
pp. 2579-2589
Author(s):  
Micheal Olaolu Arowolo ◽  
Marion Olubunmi Adebiyi ◽  
Ayodele Ariyo Adebiyi
Keyword(s):  
Gene Expression ◽  
Decision Tree ◽  
Dimensionality Reduction ◽  
Principal Component ◽  
Feature Space ◽  
Relevant Information ◽  
Component Analysis ◽  
Rna Seq ◽  
Reduction Methods ◽  
Mosquito Anopheles Gambiae

RNA-Seq data are utilized for biological applications and decision making for classification of genes. Lots of work in recent time are focused on reducing the dimension of RNA-Seq data. Dimensionality reduction approaches have been proposed in fetching relevant information in a given data. In this study, a novel optimized dimensionality reduction algorithm is proposed, by combining 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 Decision tree 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 from the high-dimensional input feature space. A feature ranking and earlier experience are used. 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 feature selection and classification in gene expression data analysis and specify that the approach is a capable accumulation to prevailing data mining techniques.

scholarly journals Tuning parameters of dimensionality reduction methods for single-cell RNA-seq analysis

2020 ◽  
Author(s):  
Felix Raimundo ◽  
Celine Vallot ◽  
Jean Philippe Vert
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
Biological Diversity ◽  
Unmet Need ◽  
Principal Component ◽  
Parameter Tuning ◽  
Differential Analysis ◽  
Rna Seq ◽  
Reduction Methods ◽  
Complex Models

AbstractBackgroundMany computational methods have been developed recently to analyze single-cell RNA-seq (scRNA-seq) data. Several benchmark studies have compared these methods on their ability for dimensionality reduction, clustering or differential analysis, often relying on default parameters. Yet given the biological diversity of scRNA-seq datasets, parameter tuning might be essential for the optimal usage of methods, and determining how to tune parameters remains an unmet need.ResultsHere, we propose a benchmark to assess the performance of five methods, systematically varying their tunable parameters, for dimension reduction of scRNA-seq data, a common first step to many downstream applications such as cell type identification or trajectory inference. We run a total of 1.5 million experiments to assess the influence of parameter changes on the performance of each method, and propose two strategies to automatically tune parameters for methods that need it.ConclusionsWe find that principal component analysis (PCA)-based methods like scran and Seurat are competitive with default parameters but do not benefit much from parameter tuning, while more complex models like ZinbWave, DCA and scVI can reach better performance but after parameter tuning.

scholarly journals Kernel principal component analysis for multimedia retrieval

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Guang-Ho Cha
Keyword(s):  
Principal Component Analysis ◽  
Dimensionality Reduction ◽  
Principal Components ◽  
Principal Component ◽  
Feature Space ◽  
Component Analysis ◽  
Multimedia Retrieval ◽  
Kernel Principal Component Analysis ◽  
Kernel Pca ◽  
Data Set

Principal component analysis (PCA) is an important tool in many areas including data reduction and interpretation, information retrieval, image processing, and so on. Kernel PCA has recently been proposed as a nonlinear extension of the popular PCA. The basic idea is to first map the input space into a feature space via a nonlinear map and then compute the principal components in that feature space. This paper illustrates the potential of kernel PCA for dimensionality reduction and feature extraction in multimedia retrieval. By the use of Gaussian kernels, the principal components were computed in the feature space of an image data set and they are used as new dimensions to approximate image features. Extensive experimental results show that kernel PCA performs better than linear PCA with respect to the retrieval quality as well as the retrieval precision in content-based image retrievals.Keywords: Principal component analysis, kernel principal component analysis, multimedia retrieval, dimensionality reduction, image retrieval

scholarly journals Principal Component Analysis for Dimensionality Reduction for Animal Classification based on LR

2019 ◽  
Vol 8 (10) ◽  
pp. 1118-1123 ◽  
Keyword(s):  
Principal Component Analysis ◽  
Dimensionality Reduction ◽  
Computation Time ◽  
Principal Component ◽  
Feature Space ◽  
Detailed Comparison ◽  
Component Analysis ◽  
Machine Learning Algorithms ◽  
Data Generation ◽  
Reduction Techniques

Nowadays, data generation is huge in nature and there is a need for analysis, visualization and prediction. Data scientists find many difficulties in processing the data at once due to its massive nature, unstructured or raw. Thus, feature extraction plays a vital role in many applications of machine learning algorithms. The process of decreasing the dimensions of the feature space by considering the prime features is defined as the dimensionality reduction. It is understood that with the dimensionality reduction techniques, redundancy could be removed and the computation time is decreased. This work gives a detailed comparison of the existing dimension reduction techniques and in addition, the importance of Principal Component Analysis is also investigated by implementing on the animal classification. In the present work, as the first phase the important features are extracted and then the logistic regression (LR) is implemented to classify the animals.

scholarly journals Effects of Collagen–Glycosaminoglycan Mesh on Gene Expression as Determined by Using Principal Component Analysis-Based Unsupervised Feature Extraction

Polymers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 4117
Author(s):  
Y-h. Taguchi ◽  
Turki Turki
Keyword(s):  
Gene Expression ◽  
Principal Component Analysis ◽  
Feature Extraction ◽  
Cell Lines ◽  
Time Course ◽  
Expression Profiles ◽  
Principal Component ◽  
Component Analysis ◽  
Altered Expression ◽  
Unsupervised Feature Extraction

The development of the medical applications for substances or materials that contact cells is important. Hence, it is necessary to elucidate how substances that surround cells affect gene expression during incubation. In the current study, we compared the gene expression profiles of cell lines that were in contact with collagen–glycosaminoglycan mesh and control cells. Principal component analysis-based unsupervised feature extraction was applied to identify genes with altered expression during incubation in the treated cell lines but not in the controls. The identified genes were enriched in various biological terms. Our method also outperformed a conventional methodology, namely, gene selection based on linear regression with time course.

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