Introduction: Neural networks are becoming a very popular method for solving machine
learning and artificial intelligence problems. The variety of neural network types and their …

Early quantitative structure-activity relationship (QSAR) technologies have unsatisfactory
versatility and accuracy in fields such as drug discovery because they are based on …

We propose a machine learning framework for risk prediction for binary response sequence
observed over time, creating a trajectory for disease progression and regression. The …

Background and objective In binary classification problems with a rare class of interest, there
is relatively little information available for the rare class to build a model. On the other hand …

The perception of odor is an important component of smell; the first step of odor detection,
and the discrimination of structurally diverse odorants depends on their interactions with …

We establish a comprehensive quantitative structure–activity relationship (QSAR) model
termed AlphaQ through the machine learning algorithm to associate the fully quantum …

We propose an ensemble of classification models formed using different assessment
metrics. For a given metric, a classifier performs feature selection and combines models …

Alzheimer's disease is a chronic neurodegenerative disease affecting more than 30 million
people worldwide. Development of small molecule inhibitors of human β‐secretase 1 …

This research presents a quantitative structure-activity relationship (QSAR) of IC50 values of
Platinum (II) derivatives. Twenty one different platinum (II) anticancer derivatives have been …

We propose a machine learning framework for risk prediction for binary response sequence
observed over time, creating a trajectory for disease progression and regression. The …