Bi-dimensional principal gene feature selection from big gene expression data

Hou, Xiaoqian; Hou, Jingyu; Huang, Guangyan

doi:10.1371/journal.pone.0278583

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…Utilizing LUAD RNA-seq data from the Cancer Genome Atlas (TCGA), mutual information (MI) was employed followed by recursive feature elimination (RFE) feature selection procedures along with SVM classifier. A new Bi-dimensional Principal Feature Selection (BPFS) procedure for efficiently extracting critical genes was proposed for high dimensional gene expression datasets [ 19 ]. This procedure utilizes the principal component analysis (PCA) technique on sample and gene domains successively, in order to identify the informative genes and reduce redundancies while losing less information.…”

Section: Introductionmentioning

confidence: 99%

Feature selection for high dimensional microarray gene expression data via weighted signal to noise ratio

et al. 2023

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Feature selection in high dimensional gene expression datasets not only reduces the dimension of the data, but also the execution time and computational cost of the underlying classifier. The current study introduces a novel feature selection method called weighted signal to noise ratio (WSNR) by exploiting the weights of features based on support vectors and signal to noise ratio, with an objective to identify the most informative genes in high dimensional classification problems. The combination of two state-of-the-art procedures enables the extration of the most informative genes. The corresponding weights of these procedures are then multiplied and arranged in decreasing order. Larger weight of a feature indicates its discriminatory power in classifying the tissue samples to their true classes. The current method is validated on eight gene expression datasets. Moreover, results of the proposed method (WSNR) are also compared with four well known feature selection methods. We found that the (WSNR) outperform the other competing methods on 6 out of 8 datasets. Box-plots and Bar-plots of the results of the proposed method and all the other methods are also constructed. The proposed method is further assessed on simulated data. Simulation analysis reveal that (WSNR) outperforms all the other methods included in the study.

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Section: Introductionmentioning

confidence: 99%

Feature selection for high dimensional microarray gene expression data via weighted signal to noise ratio

et al. 2023

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“…In the rapidly evolving landscape of genomics and bioinformatics, the emergence of gene expression data as a prime example of big data presents both opportunities and formidable challenges. Big data, characterized by its immense size and complexity, demands innovative approaches for efficient processing and analysis [1]. Gene expression data, in particular, involves measuring the activity levels of thousands of genes across various biological samples or conditions.…”

Section: Introductionmentioning

confidence: 99%

Wrapper-based Modified Binary Particle Swarm Optimization for Dimensionality Reduction in Big Gene Expression Data Analytics

Salem,

Mead,

El-Taweel

2023

IJACSA

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Gene expression data has emerged as a crucial aspect of big data in genomics. The advent of high-throughput technologies such as microarrays and next-generation sequencing has enabled the generation of extensive gene expression data. These datasets are characterized by their complexity, fast data generation, diversity, and high dimensionality. Analyzing high dimensional gene expression data offers both challenges and opportunities. Computational intelligence and deep learning techniques have been employed to extract meaningful information from these enormous datasets. However, the challenges related to preprocessing, reducing dimensionality, and normalization continue to exist. This study explored the effectiveness of the Wrapper-based Modified Particle Swarm Optimization (WMBPSO) algorithm in reducing dimensionality of big gene expression data for Alzheimer's disease (AD) prediction, using the GSE33000 dataset. The reduced dataset was then used as input to a CNN-LSTM model for prediction. The WMBPSO method identified 4303 genes out of a total of 39280 genes as being relevant for AD. These genes were selected based on their discriminatory power and potential contribution to the classification task, achieving an accuracy score of 0.98. The performance of the CNN-LSTM model is evaluated using these selected genes, and the results were highly promising. The results of our analysis are 0.968 for mean cross-validation accuracy, 0.995 for AUC, and 0.967 for recall, precision, and F1 score. Importantly, our approach outperforms conventional feature selection methods and alternative machine and deep learning algorithms. By addressing the critical challenge of dimensionality reduction in gene expression data, our study contributes to advancing the field of AD prediction and underscores the potential for improved diagnosis and patient care.

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