Disease diagnosis and prediction methods in biotechnology and medicine have significantly
advanced over time. Consequently, analyzing raw gene expression is crucial for identifying …

Gene Expression is the process of determining the physical characteristics of living beings
by generating the necessary proteins. Gene Expression takes place in two steps, translation …

In classification, regression, and other data mining applications, feature selection (FS) is an
important pre-process step which helps avoid advert effect of noisy, misleading, and …

Abstract The Hilbert–Schmidt independence criterion (HSIC) was originally designed to
measure the statistical dependence of the distribution-based Hilbert space embedding in …

Feature selection is employed to reduce the feature dimensions and computational
complexity by eliminating irrelevant and redundant features. A vast amount of increasing …

The emergence of``curse of dimensionality” issue as a result of high reduces datasets
deteriorates the capability of learning algorithms, and also requires high memory and …

Abstract Model selection is an important issue in support vector machine-based recursive
feature elimination (SVM-RFE). However, performing model selection on a linear SVM-RFE …

Partial label learning deals with training examples each associated with a set of candidate
labels, among which only one is valid. Most existing works focus on manipulating the label …

The bimodal ultrasound, namely B-mode ultrasound (BUS) and elastography ultrasound
(EUS), provide complementary information to improve the diagnostic accuracy of breast …

Recent advancements in biomedical technologies and the proliferation of high-dimensional
Next Generation Sequencing (NGS) datasets have led to significant growth in the bulk and …