Prescriptions

Recent progress in artificial intelligence and machine learning for novel diabetes mellitus medications development.

Diabetes mellitus, stemming from either insulin resistance or inadequate insulin secretion, represents a complex ailment that results in prolonged hyperglycemia and severe complications. Patients endure severe ramifications such as kidney disease, vision impairment, cardiovascular disorders, and susceptibility to infections, leading to significant physical suffering and imposing substantial socio-...

Convolutional neural networks can identify brain interactions involved in decoding spatial auditory attention.

Human listeners have the ability to direct their attention to a single speaker in a multi-talker environment. The neural correlates of selective attention can be decoded from a single trial of electroencephalography (EEG) data. In this study, leveraging the source-reconstructed and anatomically-resolved EEG data as inputs, we sought to employ CNN as an interpretable model to uncover task-specific ...

Predicting Drug-Target Interactions Via Dual-Stream Graph Neural Network.

Drug target interaction prediction is a crucial stage in drug discovery. However, brute-force search over a compound database is financially infeasibl...

Unraveling the physiological and psychosocial signatures of pain by machine learning.

BACKGROUND: Pain is a complex subjective experience, strongly impacting health and quality of life. Despite many attempts to find effective solutions,...

Deep learning approaches for the detection of scar presence from cine cardiac magnetic resonance adding derived parametric images.

This work proposes a convolutional neural network (CNN) that utilizes different combinations of parametric images computed from cine cardiac magnetic ...

G20 roadmap for carbon neutrality: The role of Paris agreement, artificial intelligence, and energy transition in changing geopolitical landscape.

The rapid advancement of artificial intelligence (AI) in the 21st century is driving profound societal changes and playing a crucial role in optimizin...

A Computational Framework for Predicting Novel Drug Indications Using Graph Convolutional Network With Contrastive Learning.

Inferring potential drug indications plays a vital role in the drug discovery process. It can be time-consuming and costly to discover novel drug indi...

DeepBP: A transformer-based model for identifying blood-brain barrier penetrating peptides with data augmentation using feedback GAN.

INTRODUCTION: The blood-brain barrier (BBB) serves as a critical structural barrier and impedes the entry of most neurotherapeutic drugs into the brai...

Discrimination of Common Strains in Urine by Liquid Chromatography-Ion Mobility-Tandem Mass Spectrometry and Machine Learning.

Accurate identification of bacterial strains in clinical samples is essential to provide an appropriate antibiotherapy to the patient and reduce the p...

GCGACNN: A Graph Neural Network and Random Forest for Predicting Microbe-Drug Associations.

The interaction between microbes and drugs encompasses the sourcing of pharmaceutical compounds, microbial drug degradation, the development of , and ...

Central-Smoothing Hypergraph Neural Networks for Predicting Drug-Drug Interactions.

Predicting drug-drug interactions (DDIs) is the problem of predicting side effects (unwanted outcomes) of a pair of drugs using drug information and k...

DSIL-DDI: A Domain-Invariant Substructure Interaction Learning for Generalizable Drug-Drug Interaction Prediction.

Drug-drug interactions (DDIs) trigger unexpected pharmacological effects in vivo, often with unknown causal mechanisms. Deep learning methods have bee...

Meta Learning With Graph Attention Networks for Low-Data Drug Discovery.

Finding candidate molecules with favorable pharmacological activity, low toxicity, and proper pharmacokinetic properties is an important task in drug ...

Mobile applications on app stores for deprescribing: A scoping review.

Deprescribing is an evidence-based intervention to reduce potentially inappropriate medication use. Yet its implementation faces barriers including in...

A machine learning technology for addressing medication-related risk in older, multimorbid patients.

OBJECTIVES: To evaluate the FeelBetter machine learning system's ability to accurately identify older patients with multimorbidity at Brigham and Wome...

Description and Validation of a Novel AI Tool, LabelComp, for the Identification of Adverse Event Changes in FDA Labeling.

INTRODUCTION: The accurate identification and timely updating of adverse reactions in drug labeling are crucial for patient safety and effective drug ...

scHiCyclePred: a deep learning framework for predicting cell cycle phases from single-cell Hi-C data using multi-scale interaction information.

The emergence of single-cell Hi-C (scHi-C) technology has provided unprecedented opportunities for investigating the intricate relationship between ce...

Strain-Temperature Dual Sensor Based on Deep Learning Strategy for Human-Computer Interaction Systems.

Thermoelectric (TE) hydrogels, mimicking human skin, possessing temperature and strain sensing capabilities, are well-suited for human-machine interac...

QCL Infrared Spectroscopy Combined with Machine Learning as a Useful Tool for Classifying Acetaminophen Tablets by Brand.

The development of new methods of identification of active pharmaceutical ingredients (API) is a subject of paramount importance for research centers,...

Testing behaviour change with an artificial intelligence chatbot in a randomized controlled study.

Chatbots can effect large-scale behaviour change because they are accessible through social media, flexible, scalable, and gather data automatically. ...