The wealth of knowledge and omic data available in drug research allowed the rising of
several computational methods in drug discovery field yielding a novel and exciting …

Proteochemometrics is a machine learning based modeling approach relying on a
combination of ligand and protein descriptors. With ongoing developments in machine …

Hospital readmission is one of the most important service quality measures. Recently,
numerous risk assessment models have been proposed to address the hospital readmission …

In multi-label learning, different labels may have their own inherent characteristics for
distinguishing each other, in the meanwhile, exploiting the correlations among labels is …

Imbalanced time-series classification (ITSC) is ubiquitous in many real-world applications. In
this study, a novel cost-sensitive deep learning framework, namely ACS-ATCN, is proposed …

The COVID-19 has now spread all over the world and causes a huge burden for public
health and world economy. Drug repositioning has become a promising treatment strategy …

In recent years, many online streaming feature selection approaches focus on flat data,
which means that all data are taken as a whole. However, in the era of big data, not only the …

Predicting 30-day hospital readmission is a core research task in the development of
personalized healthcare. However, the imbalanced class distribution and the heterogeneity …

Predicting hospital readmission with effective machine learning techniques has attracted a
great attention in recent years. The fundamental challenge of this task stems from …

In the era of open machine learning, a kind of data is accompanied by a hierarchical
structure between classes in the label space and the increasing number of features …