Recently, many methods have been proposed for microarray data analysis. One of the
challenges for microarray applications is to select a proper number of the most relevant …

Gene expression profiles can be utilized in the diagnosis of critical diseases such as cancer.
The selection of biomarker genes from these profiles is significant and crucial for cancer …

We present algorithms for iris segmentation, feature extraction and selection, and iris pattern
matching. To segment the inner boundary from a nonideal iris image, we apply a level set …

With the extensive use of Machine Learning (ML) in the biomedical field, there was an
increasing need for Explainable Artificial Intelligence (XAI) to improve transparency and …

Feature selection has become the essential step in biomarker discovery from high‐
dimensional genomics data. It is recognized that different feature selection techniques may …

Classification of cancer based on gene expression has provided insight into possible
treatment strategies. Thus, developing machine learning methods that can successfully …

Cancer classification is one of the crucial tasks in medical field. The gene expression of cells
helps in identifying the cancer. The high dimensionality of gene expression data hinders the …

As a commonly used technique in data preprocessing for machine learning, feature
selection identifies important features and removes irrelevant, redundant or noise features to …

Genetic algorithms are widely used in the estimation of expression profiles from microarrays
data. However, these techniques are unable to produce stable and robust solutions suitable …

In text classification with machine learning, utilizing terms as features using vector space
representation can result in the high dimensionality of feature space. This condition …