Prescriptions

Robot touch with speech boosts positive emotions.

A gentle touch is an essential part of human interaction that produces a positive care effect. Previously, robotics studies have shown that robots can reproduce a gentle touch that elicits similar, positive emotional responses in humans. However, whether the positive emotional effects of a robot's touch combined with speech can be enhanced using a multimodal approach remains unclear. This study su...

An Improved Multitask Learning Model with Matching Network and Its Application in Traditional Chinese Medicine Syndrome Recommendation.

Multitask learning (MTL) is an open and challenging problem in various real-world applications, such as recommendation systems, natural language processing, and computer vision. The typical way of conducting multitask learning is establishing some global parameter sharing mechanism among all tasks or assigning each task an individual set of parameters with cross-connections between tasks. However,...

Machine learning approaches and their applications in drug discovery and design.

This review is focused on several machine learning approaches used in chemoinformatics. Machine learning approaches provide tools and algorithms to im...

Self-Powered Force Sensors for Multidimensional Tactile Sensing.

A tactile sensor is the centerpiece in human-machine interfaces, enabling robotics or prosthetics to manipulate objects dexterously. Specifically, it ...

Numerical learning of deep features from drug-exposed cell images to calculate IC50 without staining.

To facilitate rapid determination of cellular viability caused by the inhibitory effect of drugs, numerical deep learning algorithms was used for unla...

Assigning diagnosis codes using medication history.

Diagnosis assignment is the process of assigning disease codes to patients. Automatic diagnosis assignment has the potential to validate code assignme...

Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction.

Disease risk prediction is a rising challenge in the medical domain. Researchers have widely used machine learning algorithms to solve this challenge....

Learning Multi-Scale Heterogeneous Representations and Global Topology for Drug-Target Interaction Prediction.

Identification of interactions between drugs and target proteins plays a critical role not only in drug discovery but also in drug repositioning. Deep...

Damage identification using wave damage interaction coefficients predicted by deep neural networks.

The ever-increasing demand for efficiency and cost improvements in lightweight structures with guaranteed safety and reliability is leading to the app...

HGDTI: predicting drug-target interaction by using information aggregation based on heterogeneous graph neural network.

BACKGROUND: In research on new drug discovery, the traditional wet experiment has a long period. Predicting drug-target interaction (DTI) in silico ca...

A Robust Fire Detection Model via Convolution Neural Networks for Intelligent Robot Vision Sensing.

Accurate fire identification can help to control fires. Traditional fire detection methods are mainly based on temperature or smoke detectors. These d...

Formulation and optimization of silymarin-encapsulated binary micelles for enhanced amyloid disaggregation activity.

Silymarin (SLY) is a natural hydrophobic polyphenol that possesses antioxidant and amyloid fibril (Aβ) inhibition activity, but its activity is hinder...

Caring for robots: How care comes to matter in human-machine interfacing.

Care robots promise to assist older people in an ageing society. This article investigates the socio-material conditions of care with robots by focusi...

Dowker complex based machine learning (DCML) models for protein-ligand binding affinity prediction.

With the great advancements in experimental data, computational power and learning algorithms, artificial intelligence (AI) based drug design has begu...

DGInet: Dynamic graph and interaction-aware convolutional network for vehicle trajectory prediction.

This paper investigates vehicle trajectory prediction problems in real traffic scenarios by fully harnessing the spatio-temporal dependencies between ...

TAHDNet: Time-aware hierarchical dependency network for medication recommendation.

Medication recommendation is a hot topic in the research of applying neural networks to the healthcare area. Although extensive progressions have been...

Combining biomedical knowledge graphs and text to improve predictions for drug-target interactions and drug-indications.

Biomedical knowledge is represented in structured databases and published in biomedical literature, and different computational approaches have been d...

Machine learning approach to identify adverse events in scientific biomedical literature.

Monitoring the occurrence of adverse events in the scientific literature is a mandatory process in drug marketing surveillance. This is a very time-co...

Multimodal treatment efficacy differs in dependence of core symptom profiles in adult Attention-Deficit/Hyperactivity Disorder: An analysis of the randomized controlled COMPAS trial.

There is broad consensus that to improve the treatment of adult Attention-Deficit/Hyperactivity Disorder (ADHD), the various therapy options need to b...

Predicting Biomedical Interactions With Higher-Order Graph Convolutional Networks.

Biomedical interaction networks have incredible potential to be useful in the prediction of biologically meaningful interactions, identification of ne...