Latest AI and machine learning research in prescriptions for healthcare professionals.
Predicting the interaction between a compound and a target is crucial for rapid drug repurposing. Deep learning has been successfully applied in drug-target affinity (DTA)problem. However, previous deep learning-based methods ignore modeling the direct interactions between drug and protein residues. This would lead to inaccurate learning of target representation which may change due to the drug bi...
Previous studies have either learned drug's features from their string or numeric representations, which are not natural forms of drugs, or only used genomic data of cell lines for the drug response prediction problem. Here, we proposed a deep learning model, GraOmicDRP, to learn drug's features from their graph representation and integrate multiple -omic data of cell lines. In GraOmicDRP, drugs a...
Identifying new disease indications for existing drugs can help facilitate drug development and reduce development cost. The previous drug-disease ass...
With the development of artificial intelligence technology in the medical field, clinical trials using artificial intelligence as an intervention meth...
Human-computer interaction (HCI) has seen a paradigm shift from textual or display-based control toward more intuitive control modalities such as voic...
The aim is to improve the teaching quality of music majors and cultivate their innovative ability. This article takes Vocal Music Education (VME) meth...
We compared the predictive performance of an artificial neural network to traditional pharmacometric modeling for population prediction of plasma conc...
The underwater environment is complicated and changeable and contains many noises, making it difficult to detect a particular object in the underwater...
The study aimed to investigate the effectiveness of an individualized power training program based on force-velocity (FV) profiling on physical functi...
High-fidelity results from atomistic simulations can only be obtained by using accurate force-field (FF) parameters. Although empirical FFs are common...
Exoskeletons have been assessed by qualitative and quantitative features known as performance indicators. Within these, the ergonomic indicators have ...
In order to explore the application of intelligent intravenous drug dispensing robot in clinical nursing, the efficiency, residual amount, needle push...
Drug-target interaction (DTI) prediction plays a crucial role in drug repositioning and virtual drug screening. Most DTI prediction methods cast the p...
The human-machine interface (HMI) has been studied for robot teleoperation with the aim of empowering people who experience motor disabilities to incr...
In this paper, we present a deep learning algorithm for automated design of druglike analogues (DeLA-Drug), a recurrent neural network (RNN) model com...
Toxoplasmosis is a zoonotic illness caused by . Those with a normal immune system normally recover without treatment. Immunocompromised individuals an...
To shed more light on the addictive power of the gabapentinoids (GPTs) gabapentin and pregabalin, we performed a structured face-to-face interview wit...
Overground powered lower limb exoskeletons (EXOs) have proven to be valid devices in gait rehabilitation in individuals with spinal cord injury (SCI)....
The first-order optimizers in deep neural networks (DNN) are of pivotal essence for a concrete loss function to reach the local minimum or global one ...
Typical image aesthetics assessment (IAA) is modeled for the generic aesthetics perceived by an "average" user. However, such generic aesthetics model...