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DNA microarray is a technique for measuring simultaneously the expression levels of a huge number of genes, these levels have a significant impact on cancer classification tasks. In DNA datasets, the number of genes exceeds the number of samples that ...

Log fold change (LFC) is a common measure used in differential expression analysis to examine the differences in gene expression between two experimental classes, as in the data generated by microarray or bulk RNA sequencing. Many single-cell RNA-seq (...

The requisite of detecting Autism in the initial stage proposed dataset is exceptionally high in the recent era since it affects children with severe impacts on social and communication developments by damaging the neural system in a broader range. Thus, ...

One of the central tasks of genome research is to predict phenotypes and discover some important gene biomarkers. However, there are three main problems in analyzing genomics data to predict phenotypes and gene marker selection. Such as large p ...

Microarrays allow the expression level analysis of thousands of genes simultaneously; thus, it is a common technique used for cancer detection and diagnosis. However, existing microarray datasets have huge data dimension and class imbalance, therefore, ...

Evolutionary and Swarm algorithms show great effectiveness when performing feature selection, classification problems, and other optimization tasks. These scenarios highlight several algorithms such as Genetic Algorithm, Particle Swarm Optimization, ...

Breast cancer is a major disease causing panic among women worldwide. Since gene mutations are the root cause for cancer development, analyzing gene expressions can give more insights into various phenotype of ...

Single-cell RNA-seq data are characterized by high dimensionality, large data volume, and noise. It is of critical importance for identifying marker genes to achieve dimensionality reduction and target the corresponding informative markers. In this ...

In genome research, it is a fundamental issue to identify few but important survival‐related biomarkers. The Cox model is a widely used survival analysis technique, which is used to study the relationship between characteristics and survival ...

In genome research, it is particularly important to identify molecular biomarkers or signaling pathways related to phenotypes. Logistic regression model is a powerful discrimination method that can offer a clear ...

There are a wide range of genes in single-cell data, but some are not beneficial to classification. In order to eliminate these redundant genes and select beneficial genes, this study first utilises the information gain (IG) to select some genes coarsely, ...

Millions of women are suffering from breast cancer pressing burden on their shoulders and the global economy. Meanwhile, general treatment methods are applied without considering personalised health and genetic features. Artificial intelligence appears to ...

Microarray data analysis infamously challenges it comprises a significant number of genes, but with small samples. Various methods have been proposed for gene selection; however, most existing methods predominantly focused on selecting relevant gene ...

The use of DNA microarray technology allows to measure the expression levels of thousands of genes in one single experiment which makes possible to apply classification techniques to classify tumours. However, the large number of genes and relatively ...

Feature selection has become one of the most important computational techniques in processing the analysis of high dimensional microarray data. In this paper, we propose a novel unsupervised feature selection method, which utilizes discriminant analysis ...

Among the large amount of genes presented in microarray gene expression data, only a small fraction of them is effective for performing a certain diagnostic test. It is for this reason that reducing the dimensionality of gene expression data is ...

Supervised feature extraction methods have received considerable attention in the data mining community due to their capability to improve the classification performance of the unsupervised dimensionality reduction methods. With increasing ...

This paper is devoted to identifying the biomarkers of rat liver regeneration via the adaptive logistic regression. By combining the adaptive elastic net penalty with the logistic regression loss, the adaptive logistic regression is proposed to ...

This paper proposes a partly adaptive group-penalised multinomial regression for gene selection. Weights with biological significance are constructed by combing the gene expression information with gene ontology network via GeneRank. By introducing the ...

A hybrid framework composed of two stages for gene selection and classification of DNA microarray data is proposed. At the first stage, five traditional statistical methods are combined for preliminary gene selection (Multiple Fusion Filter). Then, ...