Abstract: Two architectures that generalize convolutional neural networks (CNNs) for the processing of signals supported on graphs are introduced. We start with the selection graph neural network (GNN ...

Predicting the response of cell lines to characteristic drugs based on multi-omics gene information has become the core problem of precision oncology. At present, drug response prediction using ...

The cerebral cortex is strongly recurrently connected with complex wiring rules. This circuitry can now be probed by studying responses to optogenetic perturbations of one or small numbers of cells.

Abstract: Acoustic impedance (AI) is an important parameter for seismic reservoir characterization. Traditional algorithms can obtain AI whereas the resolution is open to improvement. Single-channel ...

Removing motion artifacts (MAs) from functional near-infrared spectroscopy (fNIRS) signals is crucial in practical applications, but a standard procedure is not available yet. Artificial neural ...

Diagnosis of shockable rhythms leading to defibrillation remains integral to improving out‐of‐hospital cardiac arrest outcomes. New machine learning techniques have emerged to diagnose arrhythmias on ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. As a supervised machine learning algorithm, conditional random fields are mainly used ...