Prescriptions

Deep learning identifies explainable reasoning paths of mechanism of action for drug repurposing from multilayer biological network.

The discovery and repurposing of drugs require a deep understanding of the mechanism of drug action (MODA). Existing computational methods mainly model MODA with the protein-protein interaction (PPI) network. However, the molecular interactions of drugs in the human body are far beyond PPIs. Additionally, the lack of interpretability of these models hinders their practicability. We propose an inte...

[Research and application of photovoltaic cell online monitoring system for animal robot stimulator].

Power supply plays a key role in ensuring animal robots to obtain effective stimulation. To extending the stimulating time, there is a need to apply photovoltaic cells and monitor their parameter variations, which can help operators to obtain the optimal stimulation strategy. In this paper, an online monitoring system of photovoltaic cells for animal robot stimulators was presented. It was compose...

multi-type neighbors enhanced global topology and pairwise attribute learning for drug-protein interaction prediction.

MOTIVATION: Accurate identification of proteins interacted with drugs helps reduce the time and cost of drug development. Most of previous methods foc...

Biological activities of drug inactive ingredients.

In a drug formulation (DFM), the major components by mass are not Active Pharmaceutical Ingredient (API) but rather Drug Inactive Ingredients (DIGs). ...

DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations.

Drug combination therapies are superior to monotherapy for cancer treatment in many ways. Identifying novel drug combinations by screening is challeng...

A chronotherapeutics-applicable multi-target therapeutics based on AI: Example of therapeutic hypothermia.

Nowadays, the complexity of disease mechanisms and the inadequacy of single-target therapies in restoring the biological system have inevitably instig...

Multidrug representation learning based on pretraining model and molecular graph for drug interaction and combination prediction.

MOTIVATION: Approaches for the diagnosis and treatment of diseases often adopt the multidrug therapy method because it can increase the efficacy or re...

Challenges and opportunities for mining adverse drug reactions: perspectives from pharma, regulatory agencies, healthcare providers and consumers.

Monitoring drug safety is a central concern throughout the drug life cycle. Information about toxicity and adverse events is generated at every stage ...

STNN-DDI: a Substructure-aware Tensor Neural Network to predict Drug-Drug Interactions.

Computational prediction of multiple-type drug-drug interaction (DDI) helps reduce unexpected side effects in poly-drug treatments. Although existing ...

Mitigating cold-start problems in drug-target affinity prediction with interaction knowledge transferring.

Predicting the drug-target interaction is crucial for drug discovery as well as drug repurposing. Machine learning is commonly used in drug-target aff...

AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification.

In this study, we introduce an interpretable graph-based deep learning prediction model, AttentionSiteDTI, which utilizes protein binding sites along ...

De novo molecular design with deep molecular generative models for PPI inhibitors.

We construct a protein-protein interaction (PPI) targeted drug-likeness dataset and propose a deep molecular generative framework to generate novel dr...

Effective drug-target interaction prediction with mutual interaction neural network.

MOTIVATION: Accurately predicting drug-target interaction (DTI) is a crucial step to drug discovery. Recently, deep learning techniques have been wide...

A Generic Semi-Supervised and Active Learning Framework for Biomedical Text Classification.

Biomedical text classification requires having training examples labeled by clinical specialists, a process that can be costly. To address this proble...

Increase and Decrease in Velocity and Force During Exercise with a Hybrid Robotic-FES Rehabilitation System.

Early rehabilitation is beneficial for stroke patients, but it is often delayed since the patients are often bedbound due to their general condition. ...

Overcoming Major Barriers to Build Efficient Decision Support Systems in Pharmacovigilance.

Many decision support methods and systems in pharmacovigilance are built without explicitly addressing specific challenges that jeopardize their event...

SPARSE: a sparse hypergraph neural network for learning multiple types of latent combinations to accurately predict drug-drug interactions.

MOTIVATION: Predicting side effects of drug-drug interactions (DDIs) is an important task in pharmacology. The state-of-the-art methods for DDI predic...

Artificial intelligence and thyroid disease management: considerations for thyroid function tests.

Artificial intelligence (AI) is transforming healthcare and offers new tools in clinical research, personalized medicine, and medical diagnostics. Thy...

Named Entity Recognition in Pubmed Abstracts for Pharmacovigilance Using Deep Learning.

Methods of natural language processing associated with machine learning or deep learning can support detection of adverse drug reactions in abstracts ...

BioDKG-DDI: predicting drug-drug interactions based on drug knowledge graph fusing biochemical information.

The way of co-administration of drugs is a sensible strategy for treating complex diseases efficiently. Because of existing massive unknown interactio...