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A Comprehensive Survey of Recent Approaches on Microarray Image Data
Microarray image processing techniques are used to study gene expressions in the form of images. This helps in genomic study without sequencing to...
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Feature selection using differential evolution for microarray data classification
The dimensions of microarray datasets are very large, containing noise and redundancy. The problem with microarray datasets is the presence of more...
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Feature selection of microarray data using multidimensional graph neural network and supernode hierarchical clustering
Cancer remains a significant cause of mortality, and the application of microarray technology has opened new avenues for cancer diagnosis and...
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Class Prediction with Microarray Datasets
Microarray technology is having a significant impact in the biological and medical sciences and class prediction will play an increasingly important...
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Multimodal feature selection from microarray data based on Dempster–Shafer evidence fusion
Microarray data have a crucial role in identifying and classifying different types of cancer tissues. In cancer research, high dimensional of...
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An efficient feature selection and classification system for microarray cancer data using genetic algorithm and deep belief networks
Cancer is one of the most devastating health conditions in the world. In the diagnosis and treatment of the various forms of cancer illness, studies...
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Can Complexity Measures and Instance Hardness Measures Reflect the Actual Complexity of Microarray Data?
Despite the significant contribution of the research community in the context of the Microarray data analysis, little attention has been made in...
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Cancer Classification with Microarray Data Using Support Vector Machines
Microarrays (Schena et al. 1995) are also called gene chips or DNA chips. On a microarray chip, there are thousands of spots. Each spot contains the...
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Assessing gene stability and gene affinity in microarray data classification using an extended relieff algorithm
Microarray data have become an integral part of the clinical and drug discovery process. Due to its voluminous and heterogeneous nature, the question...
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A New Evolutionary Ensemble Learning of Multimodal Feature Selection from Microarray Data
In the last decades, data has grown exponentially with respect to the number of samples and features. This makes the feature selection (FS) more...
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Improved multi-layer hybrid adaptive particle swarm optimization based artificial bee colony for optimizing feature selection and classification of microarray data
Early diagnosis of cancer allows for easy follow-up of patients’ treatment processes. The utilization of microarray gene technology has become...
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Binary Multi-objective Hybrid Equilibrium Optimizer Algorithm for Microarray Data
Feature selection aims at identifying features relevant to the target from high-dimensional data to enhance the performance of the learner. When...
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MiniAnDE: A Reduced AnDE Ensemble to Deal with Microarray Data
This article focuses on the supervised classification of datasets with a large number of variables and a small number of instances. This is the case,...
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Missing value estimation of microarray data using Sim-GAN
Microarray data analysis needs utmost care as it plays a significant role in cancer study. Due to the excessive complexity of the data extraction...
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Feature selection methods in microarray gene expression data: a systematic map** study
Feature selection (FS) is an important area of research in medicine and genetics. Cancer classification based on the microarray gene expression data...
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Classification of cancer microarray data using a two-step feature selection framework with moth-flame optimization and extreme learning machine
Analysis of microarray gene expression data for the detection/classification of cancer is one of the common approaches adopted worldwide. However,...
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Dynamic scaling factor based differential evolution with multi-layer perceptron for gene selection from pathway information of microarray data
The microarray data contains the high volume of genes having multiple values of expressions and small number of samples. Therefore, the selection of...
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Hybrid binary COOT algorithm with simulated annealing for feature selection in high-dimensional microarray data
Microarray analysis of gene expression can help with disease and cancer diagnosis and prognosis. Identification of gene biomarkers is one of the most...
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Improved swarm-optimization-based filter-wrapper gene selection from microarray data for gene expression tumor classification
A typical microarray dataset usually contains thousands of genes, but only a small number of samples. It is in fact that most genes in a DNA...
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MiRNA subset selection for microarray data classification using grey wolf optimizer and evolutionary population dynamics
Micro-ribonucleic acids (miRNAs) are tiny noncoding ribonucleic acid (RNA) molecules that involve various biological processes for cancer...
