Abstract Explainable Artificial Intelligence (XAI) is an emerging research topic of machine
learning aimed at unboxing how AI systems' black-box choices are made. This research field …

Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and
assessment of clinical signs, including the characterization of a variety of motor symptoms …

Mash extends the MinHash dimensionality-reduction technique to include a pairwise
mutation distance and P value significance test, enabling the efficient clustering and search …

Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space,
perform particularly well in various tasks that process graph structure data. With the rapid …

The task of predicting the interactions between drugs and targets plays a key role in the
process of drug discovery. There is a need to develop novel and efficient prediction …

We present a machine learning based COVID-19 cough classifier which can discriminate
COVID-19 positive coughs from both COVID-19 negative and healthy coughs recorded on a …

Motivation Estimating the future course of patients with cancer lesions is invaluable to
physicians; however, current clinical methods fail to effectively use the vast amount of …

A fascinating challenge in the field of human–robot interaction is the possibility to endow
robots with emotional intelligence in order to make the interaction more intuitive, genuine …

Like all organisms on the planet, environmental microbes are subject to the forces of
molecular evolution. Metagenomic sequencing provides a means to access the DNA …

Abstracta Voluminous parallel sequencing datasets, especially metagenomic experiments,
require distributed computing for de novo assembly and taxonomic profiling. Ray Meta is a …