Prescriptions

A 3D Tumor-Mimicking In Vitro Drug Release Model of Locoregional Chemoembolization Using Deep Learning-Based Quantitative Analyses.

Primary liver cancer, with the predominant form as hepatocellular carcinoma (HCC), remains a worldwide health problem due to its aggressive and lethal nature. Transarterial chemoembolization, the first-line treatment option of unresectable HCC that employs drug-loaded embolic agents to occlude tumor-feeding arteries and concomitantly delivers chemotherapeutic drugs into the tumor, is still under f...

DRaW: prediction of COVID-19 antivirals by deep learning-an objection on using matrix factorization.

BACKGROUND: Due to the high resource consumption of introducing a new drug, drug repurposing plays an essential role in drug discovery. To do this, researchers examine the current drug-target interaction (DTI) to predict new interactions for the approved drugs. Matrix factorization methods have much attention and utilization in DTIs. However, they suffer from some drawbacks.

A method based on interpretable machine learning for recognizing the intensity of human engagement intention.

To interact with humans more precisely and naturally, social robots need to "perceive" human engagement intention, especially need to recognize the ma...

Rational design of stapled antimicrobial peptides.

The global increase in antimicrobial drug resistance has dramatically reduced the effectiveness of traditional antibiotics. Structurally diverse antib...

A machine learning strategy with clustering under sampling of majority instances for predicting drug target interactions.

Drug Target Interactions (DTIs) are crucial in drug discovery as it reduces the range of candidate searches, speeding up the drug screening process. C...

Comparative Studies on Resampling Techniques in Machine Learning and Deep Learning Models for Drug-Target Interaction Prediction.

The prediction of drug-target interactions (DTIs) is a vital step in drug discovery. The success of machine learning and deep learning methods in accu...

Extracting medication changes in clinical narratives using pre-trained language models.

An accurate and detailed account of patient medications, including medication changes within the patient timeline, is essential for healthcare provide...

A Knowledge-Graph-Based Multimodal Deep Learning Framework for Identifying Drug-Drug Interactions.

The identification of drug-drug interactions (DDIs) plays a crucial role in various areas of drug development. In this study, a deep learning framewor...

DeepSide: A Deep Learning Approach for Drug Side Effect Prediction.

Drug failures due to unforeseen adverse effects at clinical trials pose health risks for the participants and lead to substantial financial losses. Si...

ctPISP: Protein-Protein Interaction Sites Prediction Using Convolution and Transformer With Data Augmentation.

Protein-protein interactions are the basis of many cellular biological processes, such as cellular organization, signal transduction, and immune respo...

Adversarial Knowledge Distillation Based Biomedical Factoid Question Answering.

Biomedical factoid question answering is an essential application for biomedical information sharing. Recently, neural network based approaches have s...

Dynamic analysis and control of a hybrid lower limb rehabilitation robot to reduce human-robot interaction forces.

This study presents a model of cooperation between two planar manipulators including an orthosis and a programmable plate in form of a hybrid lower li...

In robot we trust? The effect of emotional expressions and contextual cues on anthropomorphic trustworthiness.

Following the evolution of technology and its application in various daily contexts, social robots work as an advanced artificial intelligence (AI) sy...

Industrializing AI/ML during the end-to-end drug discovery process.

Drug discovery aims to select proper targets and drug candidates to address unmet clinical needs. The end-to-end drug discovery process includes all s...

Artificial Intelligence and Data Mining for the Pharmacovigilance of Drug-Drug Interactions.

Despite increasing mechanistic understanding, undetected and underrecognized drug-drug interactions (DDIs) persist. This elusiveness relates to an int...

Altering Fish Behavior by Sensing Swarm Patterns of Fish in an Artificial Aquatic Environment Using an Interactive Robotic Fish.

Numerous studies have been conducted to prove the calming and stress-reducing effects on humans of visiting aquatic environments. As a result, many in...

Decision support system to classify the vulnerability of broiler production system to heat stress based on fuzzy logic.

In this study, we develop an artificial intelligence model to predict the vulnerability of broiler production systems (broilers and facilities) to hea...

A Hybrid Generic Framework for Heart Problem Diagnosis Based on a Machine Learning Paradigm.

The early, valid prediction of heart problems would minimize life threats and save lives, while lack of prediction and false diagnosis can be fatal. A...

DeepMPF: deep learning framework for predicting drug-target interactions based on multi-modal representation with meta-path semantic analysis.

BACKGROUND: Drug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional bio...

Intraprocedure Artificial Intelligence Alert System for Colonoscopy Examination.

Colonoscopy is a valuable tool for preventing and reducing the incidence and mortality of colorectal cancer. Although several computer-aided colorecta...