Many fields such as data science, data mining suffered from the rapid growth of data volume
and high data dimensionality. The main problems which are faced by these fields include …

Gene expression data classification is an important technology for cancer diagnosis in
bioinformatics and has been widely researched. Due to the large number of genes and the …

Tumor classification is one of the most vital technologies for cancer diagnosis. Due to the
high dimensionality, gene selection (finding a small, closely related gene set to accurately …

Polo-like kinases (PLKs) play important roles in regulating multiple aspects of cell cycle and
cell proliferation. In many cancer types, PLK family members are often dysregulated, which …

Feature selection is used for excluding redundant features and enhancing learning
performance. Abundant unlabeled data are existed in many applications which can be used …

Feature selection is a highly regarded research area in the field of data mining, as it
significantly enhances the efficiency and performance of high-dimensional data analysis by …

Quantitative structure-activity relationship (QSAR) modeling is a powerful tool for drug
discovery, yet the lack of interpretability of commonly used QSAR models hinders their …

Background: Selectively targeting dopamine receptors (DRs) has been a persistent
challenge in the last years for the development of new treatments to combat the large variety …

Semi-supervised feature selection, which exploits both labeled and unlabeled data to select
the relevant features, has an important role in many real world applications. Most semi …

With the speedy development of network technology, diverse data increase by hundreds of
millions per hour, causing increasing pressure on the acquisition of data labels. Semi …