The remarkable flexibility and adaptability of ensemble methods and deep learning models
have led to the proliferation of their application in bioinformatics research. Traditionally …

Feature selection technique is a knowledge discovery tool which provides an understanding
of the problem through the analysis of the most relevant features. Feature selection aims at …

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 …

Machine learning with maximization (support) of separating margin (vector), called support
vector machine (SVM) learning, is a powerful classification tool that has been used for …

Ensemble learning is a prolific field in Machine Learning since it is based on the assumption
that combining the output of multiple models is better than using a single model, and it …

Drug discovery and development is a time-consuming process that involves identifying,
designing, and testing new drugs to address critical medical needs. In recent years, machine …

Feature selection, as a data preprocessing strategy, has been proven to be effective and
efficient in preparing data (especially high-dimensional data) for various data-mining and …

In recent years, several associations between common chronic human disorders and altered
gut microbiome composition and function have been reported,. In most of these reports …

The data-driven modern era has enabled the collection of large amounts of biomedical and
clinical data. DNA microarray gene expression datasets have mainly gained significant …

Plenty of feature selection methods are available in literature due to the availability of data
with hundreds of variables leading to data with very high dimension. Feature selection …