Prescriptions

GCHN-DTI: Predicting drug-target interactions by graph convolution on heterogeneous networks.

Determining the interaction of drug and target plays a key role in the process of drug development and discovery. The calculation methods can predict new interactions and speed up the process of drug development. In recent studies, the network-based approaches have been proposed to predict drug-target interactions. However, these methods cannot fully utilize the node information from heterogeneous...

Neural Networks Enhanced Optimal Admittance Control of Robot-Environment Interaction Using Reinforcement Learning.

In this paper, an adaptive admittance control scheme is developed for robots to interact with time-varying environments. Admittance control is adopted to achieve a compliant physical robot-environment interaction, and the uncertain environment with time-varying dynamics is defined as a linear system. A critic learning method is used to obtain the desired admittance parameters based on the cost fun...

Cross-Modal Reconstruction for Tactile Signal in Human-Robot Interaction.

A human can infer the magnitude of interaction force solely based on visual information because of prior knowledge in human-robot interaction (HRI). A...

Multitype drug interaction prediction based on the deep fusion of drug features and topological relationships.

Drug-drug interaction (DDI) prediction has received considerable attention from industry and academia. Most existing methods predict DDIs from drug at...

A Comprehensive Quantitative and Biological Neural Network Optimization Model of Sports Industry Structure Based on Knowledge Mapping.

In this paper, a comprehensive quantitative and biological neural network optimization model of sports industry structure is thoroughly studied and an...

Optimization of Tennis Teaching Resources and Data Visualization Based on Support Vector Machine.

In recent years, with the continuous development of machine learning technology, this technology has achieved success in many fields and activities. T...

Comparative studies of deep learning segmentation models for left ventricle segmentation.

One of the primary factors contributing to death across all age groups is cardiovascular disease. In the analysis of heart function, analyzing the lef...

Prediction and evaluation of combination pharmacotherapy using natural language processing, machine learning and patient electronic health records.

Combination pharmacotherapy targets key disease pathways in a synergistic or additive manner and has high potential in treating complex diseases. Comp...

Characterization of the SGLT2 Interaction Network and Its Regulation by SGLT2 Inhibitors: A Bioinformatic Analysis.

Sodium-glucose cotransporter 2 (SGLT2), also known as solute carrier family 5 member 2 (SLC5A2), is a promising target for a new class of drugs prima...

IMSE: interaction information attention and molecular structure based drug drug interaction extraction.

BACKGROUND: Extraction of drug drug interactions from biomedical literature and other textual data is an important component to monitor drug-safety an...

Graph convolutional network based virus-human protein-protein interaction prediction for novel viruses.

Computational identification of human-virus protein-protein interactions (PHIs) is a worthwhile step towards understanding infection mechanisms. Analy...

Development of deep learning chest X-ray model for cardiac dose prediction in left-sided breast cancer radiotherapy.

Deep inspiration breath-hold (DIBH) is widely used to reduce the cardiac dose in left-sided breast cancer radiotherapy. This study aimed to develop a ...

Segmentation of Medical Image Using Novel Dilated Ghost Deep Learning Model.

Image segmentation and computer vision are becoming more important in computer-aided design. A computer algorithm extracts image borders, colours, and...

Gating Attractor Dynamics of Frontal Cortex Under Acupuncture via Recurrent Neural Network.

Acupuncture can regulate the functions of human body and improve the cognition of brain. However, the mechanism of acupuncture manipulations remains u...

Deep learning prediction of chemical-induced dose-dependent and context-specific multiplex phenotype responses and its application to personalized alzheimer's disease drug repurposing.

Predictive modeling of drug-induced gene expressions is a powerful tool for phenotype-based compound screening and drug repurposing. State-of-the-art ...

Prioritized and predictive intelligence of things enabled waste management model in smart and sustainable environment.

Collaborative modelling of the Internet of Things (IoT) with Artificial Intelligence (AI) has merged into the Intelligence of Things concept. This rec...

Transformer Neural Networks for Protein Family and Interaction Prediction Tasks.

The scientific community is rapidly generating protein sequence information, but only a fraction of these proteins can be experimentally characterized...

Pan-Logical Probabilistic Algorithms Based on Convolutional Neural Networks.

A brand-new kind of flexible logic system called universal logic aims to address a variety of uncertain problems. In this study, the role of convoluti...

An efficient modular framework for automatic LIONC classification of MedIMG using unified medical language.

Handwritten prescriptions and radiological reports: doctors use handwritten prescriptions and radiological reports to give drugs to patients who have ...

Multimodal Sentiment Analysis Based on Cross-Modal Attention and Gated Cyclic Hierarchical Fusion Networks.

Multimodal sentiment analysis has been an active subfield in natural language processing. This makes multimodal sentiment tasks challenging due to the...