Prescriptions

A pharmacokinetic-pharmacodynamic model based on the SSA-1DCNN-Attention network and the semicompartment method.

To solve the problem of inaccurate prediction caused by the lack of representativeness of samples due to the small sample size of the collected clinical data when using machine learning methods to predict drug concentration in plasma and describe the hysteresis phenomenon of drug effect lagging behind plasma drug concentration, this paper proposes a pharmacokinetic-pharmacodynamic (PK-PD) model ba...

On the potentials of interaction breakdowns for HRI.

How do we switch between "playing along" and treating robots as technical agents? We propose interaction breakdowns to help solve this "social artifact puzzle": Breaks cause changes from fluid interaction to explicit reasoning and interaction with the raw artifact. These changes are closely linked to understanding the technical architecture and could be used to design better human-robot interactio...

Universal early warning signals of phase transitions in climate systems.

The potential for complex systems to exhibit tipping points in which an equilibrium state undergoes a sudden and often irreversible shift is well esta...

Constructing metabolism-protein interaction relationship to identify glioma prognosis using deep learning.

Glioma is heterogeneous disease that requires classification into subtypes with similar clinical phenotypes, prognosis or treatment responses. Metabol...

AttentionDTA: Drug-Target Binding Affinity Prediction by Sequence-Based Deep Learning With Attention Mechanism.

The identification of drug-target relations (DTRs) is substantial in drug development. A large number of methods treat DTRs as drug-target interaction...

Enhancing Drug-Drug Interaction Prediction Using Deep Attention Neural Networks.

Drug-drug interactions are one of the main concerns in drug discovery. Accurate prediction of drug-drug interactions plays a key role in increasing th...

Explaining Black Box Drug Target Prediction Through Model Agnostic Counterfactual Samples.

Many high-performance DTA deep learning models have been proposed, but they are mostly black-box and thus lack human interpretability. Explainable AI ...

Graph Transformer for Drug Response Prediction.

Previous models have shown that learning drug features from their graph representation is more efficient than learning from their strings or numeric r...

Reverse tracking from drug-induced transcriptomes through multilayer molecular networks reveals hidden drug targets.

Identifying molecular targets of a drug is an essential process for drug discovery and development. The recent in-silico approaches are usually based ...

A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation.

The discovery and development of new drugs are extremely long and costly processes. Recent progress in artificial intelligence has made a positive imp...

BiComp-DTA: Drug-target binding affinity prediction through complementary biological-related and compression-based featurization approach.

Drug-target binding affinity prediction plays a key role in the early stage of drug discovery. Numerous experimental and data-driven approaches have b...

A compact review of progress and prospects of deep learning in drug discovery.

BACKGROUND: Drug discovery processes, such as new drug development, drug synergy, and drug repurposing, consume significant yearly resources. Computer...

Deep Learning-Based Modeling of Drug-Target Interaction Prediction Incorporating Binding Site Information of Proteins.

Chemogenomics, also known as proteochemometrics, covers various computational methods for predicting interactions between related drugs and targets on...

Automated deep learning for classification of dental implant radiographs using a large multi-center dataset.

This study aimed to evaluate the accuracy of automated deep learning (DL) algorithm for identifying and classifying various types of dental implant sy...

Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review.

OBJECTIVES: The aim of this study is to investigate the effect of artificial intelligence (AI) and/or algorithms on drug management in primary care se...

Robot-assisted investigation of sensorimotor control in Parkinson's disease.

Sensorimotor control (SMC) is a complex function that involves sensory, cognitive, and motor systems working together to plan, update and execute volu...

CNN-Siam: multimodal siamese CNN-based deep learning approach for drug‒drug interaction prediction.

BACKGROUND: Drug‒drug interactions (DDIs) are reactions between two or more drugs, i.e., possible situations that occur when two or more drugs are use...

A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer.

One of the main obstacles to the successful treatment of cancer is the phenomenon of drug resistance. A common strategy to overcome resistance is the ...

Joint action with human and robotic co-actors: Self-other integration is immune to the perceived humanness of the interacting partner.

When performing a joint action task, we automatically represent the action and/or task constraints of the co-actor with whom we are interacting. Curre...

Predicting Drug Synergy and Discovering New Drug Combinations Based on a Graph Autoencoder and Convolutional Neural Network.

Drug synergy is a crucial component in drug reuse since it solves the problem of sluggish drug development and the absence of corresponding drugs for ...