Prescriptions

Data Mining Meets Machine Learning: A Novel ANN-based Multi-body Interaction Docking Scoring Function (MBI-score) Based on Utilizing Frequent Geometric and Chemical Patterns of Interfacial Atoms in Native Protein-ligand Complexes.

Accurate prediction of binding poses is crucial to structure-based drug design. We employ two powerful artificial intelligence (AI) approaches, data-mining and machine-learning, to design artificial neural network (ANN) based pose-scoring function. It is a simple machine-learning-based statistical function that employs frequent geometric and chemical patterns of interacting atoms at protein-ligand...

CNN-DDI: a learning-based method for predicting drug-drug interactions using convolution neural networks.

BACKGROUND: Drug-drug interactions (DDIs) are the reactions between drugs. They are compartmentalized into three types: synergistic, antagonistic and no reaction. As a rapidly developing technology, predicting DDIs-associated events is getting more and more attention and application in drug development and disease diagnosis fields. In this work, we study not only whether the two drugs interact, bu...

Improving Haptic Response for Contextual Human Robot Interaction.

For haptic interaction, a user in a virtual environment needs to interact with proxies attached to a robot. The device must be at the exact location d...

Sentiment Analysis of Statements on Social Media and Electronic Media Using Machine and Deep Learning Classifiers.

When it comes to our everyday life, emotions have a critical role to play. It goes without saying that it is critical in the context of mobile-compute...

A generic neural network model to estimate populational neural activity for robust neural decoding.

BACKGROUND: Robust and continuous neural decoding is crucial for reliable and intuitive neural-machine interactions. This study developed a novel gene...

Identifying the Strength Level of Objects' Tactile Attributes Using a Multi-Scale Convolutional Neural Network.

In order to solve the problem in which most currently existing research focuses on the binary tactile attributes of objects and ignores identifying th...

Deep learning for biosignal control: insights from basic to real-time methods with recommendations.

Biosignal control is an interaction modality that allows users to interact with electronic devices by decoding the biological signals emanating from t...

Structure-Aware Multimodal Deep Learning for Drug-Protein Interaction Prediction.

Identifying drug-protein interactions (DPIs) is crucial in drug discovery, and a number of machine learning methods have been developed to predict DPI...

ACPNet: A Deep Learning Network to Identify Anticancer Peptides by Hybrid Sequence Information.

Cancer is one of the most dangerous threats to human health. One of the issues is drug resistance action, which leads to side effects after drug treat...

DBGC: Dimension-Based Generic Convolution Block for Object Recognition.

The object recognition concept is being widely used a result of increasing CCTV surveillance and the need for automatic object or activity detection f...

Screening and functional prediction of differentially expressed genes in walnut endocarp during hardening period based on deep neural network under agricultural internet of things.

The deep neural network is used to establish a neural network model to solve the problems of low accuracy and poor accuracy of traditional algorithms ...

Combining explainable machine learning, demographic and multi-omic data to inform precision medicine strategies for inflammatory bowel disease.

Inflammatory bowel diseases (IBDs), including ulcerative colitis and Crohn's disease, affect several million individuals worldwide. These diseases are...

Construction of a Non-Mutually Exclusive Decision Tree for Medication Recommendation of Chronic Heart Failure.

Although guidelines have recommended standardized drug treatment for heart failure (HF), there are still many challenges in making the correct clinic...

Robots in Eldercare: How Does a Real-World Interaction with the Machine Influence the Perceptions of Older People?

(1) Background: Using autonomous social robots in selected areas of care for community-dwelling older adults is one of the promising approaches to add...

Fuzzy Support Tensor Product Adaptive Image Classification for the Internet of Things.

Computer vision is one of the hottest research directions in artificial intelligence at present, and its research goal is to give computers the abilit...

Implementation of an early warning system with hyperspectral imaging combined with deep learning model for chlorine in refuse derived fuels.

Chlorine content is one of the most important parameters in Refuse Derived Fuels (RDFs) used as a fuel in cement kilns. The main problem with the use ...

Extraction of Active Medications and Adherence Using Natural Language Processing for Glaucoma Patients.

Accuracy of medication data in electronic health records (EHRs) is crucial for patient care and research, but many studies have shown that medication ...

First-line drug resistance profiling of : a machine learning approach.

The persistence and emergence of new multi-drug resistant Mycobacterium tuberculosis (M. tb) strains continues to advance the devastating tuberculosis...

A Novel Algorithm to Estimate the Significance Level of a Feature Interaction Using the Extreme Gradient Boosting Machine.

Recent studies have revealed the importance of the interaction effect in cardiac research. An analysis would lead to an erroneous conclusion when the ...

Hybrid Virtual Commissioning of a Robotic Manipulator with Machine Vision Using a Single Controller.

Digital twin (DT) is an emerging key technology that enables sophisticated interaction between physical objects and their virtual replicas, with appli...