High-dimensional data analysis is a challenge for researchers and engineers in the fields of
machine learning and data mining. Feature selection provides an effective way to solve this …

The patterns identified from the systematically collected molecular profiles of patient tumor
samples, along with clinical metadata, can assist personalized treatments for effective …

At present, the study of gene expression data provides a reference for tumor diagnosis at the
molecular level. It is a challenging task to select the feature genes related to the …

Microarray techniques have been used to delineate cancer groups or to identify candidate
genes for cancer prognosis. As such problems can be viewed as classification ones, various …

Contemporary advancements in technology provide vast quantities of data with large
dimensions, leading to high computing burdens. These big data quantities suffer from …

Quality data mining analysis based on microarray gene expression data is a good approach
for disease classification and other fields, such as pharmacology, as well as a useful tool for …

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 …

Background Development of biologically relevant models from gene expression data
notably, microarray data has become a topic of great interest in the field of bioinformatics …

Artificial intelligence (AI) and machine learning (ML) have been active in various research
fields and improved results. However, most of them applied or focused on supervised …

Optimizing highly nonlinear structural problems can be very challenging due to the large
number of parameters. Classical compliance minimization does not work for such problems …