Computational methods are employed in bioinformatics to predict protein–protein
interactions (PPIs). PPIs and protein–protein non-interactions (PPNIs) display different levels …

Alternative techniques and tools for analyzing biomolecular networks With the recent rapid
advances in molecular biology, high-throughput experimental methods have resulted in …

Many life activities and key functions in organisms are maintained by different types of
protein–protein interactions (PPIs). In order to accelerate the discovery of PPIs for different …

Abstract Machine learning models that predict genomic activity are most useful when they
make accurate predictions across cell types. Here, we show that when the training and test …

Protein–protein interactions (PPIs) are essential for most living organisms' process. Thus,
detecting PPIs is extremely important to understand the molecular mechanisms of biological …

Introduction Plant–microbe interactions play a vital role in the development of strategies to
manage pathogen-induced destructive diseases that cause enormous crop losses every …

The study of protein-protein interactions (PPIs) has been growing for some years now,
mainly as a result of easy access to high-throughput experimental data. Several …

Protein-protein interaction (PPI) plays a key role in understanding cellular mechanisms in
different organisms. Many supervised classifiers like Random Forest (RF) and Support …

Proteins are important cellular molecules, and interacting protein pairs provide biologically
important information, such as functional relationships. We focus on the problem of …

Background Identifying protein-protein interactions (PPIs) is of paramount importance for
understanding cellular processes. Machine learning-based approaches have been …