Data mining and machine learning algorithms' performance is degraded by data of high-
dimensional nature due to an issue called “curse of dimensionality”. Feature selection is a …

Over the past few decades, there has been a massive growth in the volume of biological
data. In such datasets, the influence of dimensionality bias or existence of repetitive, noisy …

Gene expression profiling has played a significant role in the identification and classification
of tumor molecules. In gene expression data, only a few feature genes are closely related to …

Instance selection, as an important data pre-processing task, is widely used in supervised
classification. Recently, a series of instance selection algorithms with different techniques …

Identification of informative genes is essential for the disease and cancer studies.
Metaheuristic algorithms have been widely used for this purpose. However, their …

One of the deadly diseases prevailing worldwide is cancer. The rigorous symptoms of
cancers should be studied properly prior to the diagnosis to save patients life. Thus, an …

Identifying genes with the largest expression changes (gene selection) to characterize a
given condition is a popular first step to drive exploration into molecular mechanisms and is …

With the deepening of people's research on event anaphora, a large number of methods will
be used in the identification and resolution of event anaphora. Although there has been …

Machine learning (ML) is a popular tool in healthcare while it can help to analyze large
amounts of patient data, such as medical records, predict diseases, and identify early signs …

Hepatocellular carcinoma (HCC) is a highly prevalent form of liver cancer that necessitates
accurate prediction models for early diagnosis and effective treatment. Machine learning …