Prescriptions

An interpretable machine learning framework for adverse drug reaction prediction from drug-target interactions.

BACKGROUND: Adverse drug reactions (ADRs) present challenges to patient safety and healthcare systems. Current pharmacovigilance methods, such as the Yellow Card Scheme (YCS), provide valuable post-marketing data, but the mechanistic causes of these ADRs are not fully understood. Leveraging drug-target interaction data with interpretable machine learning offers a promising approach to anticipate A...

A multilayered integrated analysis of insomnia-related genes ATG7 and JAK2 in the autophagy-inflammation mechanism and clinical implications in major depressive disorder.

OBJECTIVE: Insomnia is widely recognized as a key risk factor for major depressive disorder (MDD). However, the potential molecular mechanisms and the underlying interactions among them remain to be elucidate. METHODS: In NHANES, an insomnia-like high-risk sleep phenotype was linked to an increased risk of MDD, and Mendelian randomization (MR) provided evidence for a potential causal effect of the...

From virtual screening to bench: A dual-validation framework for drug repurposing against PI3K.

Virtual screening has emerged as one of the most impactful in silico approaches for the identification of novel drug candidates, substantially reducin...

Evaluating the methodological suitability of partial dependence plots and Shapley additive explanations for population-level interpretation of machine learning models in total joint arthroplasty.

BACKGROUND: Total joint arthroplasty (TJA) complications necessitate the development of accurate risk prediction models; however, interpretability in ...

Generic fitting models learn edge representations from prenatal retinal waves.

Orientation selectivity-the representation of oriented edges-is a hallmark of biological vision, shared across mammals, birds, and reptiles. However, ...

Anti-Inflammatory Properties of Dendrobium: A Systematic Review of Pharmacological Mechanisms.

ETHNOPHARMACOLOGICAL RELEVANCE: In Traditional Chinese Medicine (TCM), Dendrobium species have long been utilized to alleviate various inflammatory sy...

Artificial Intelligence and the Psychology of Human Connection.

As artificial intelligence (AI) becomes increasingly embedded in social life, understanding its interpersonal and psychological implications is urgent...

Detection of Antithrombotic-Related Bleeding in Older Inpatients: Multicenter Retrospective Study Using Structured and Unstructured Electronic Health Record Data.

BACKGROUND: Bleeding complications are a major contributor to adverse drug events among older inpatients, particularly in those treated with antithrom...

Predicting postoperative delirium after lung cancer resection: the utility of synthetic data and LIME algorithm for model interpretation.

To construct an efficient predictive model for post-lung cancer resection delirium (POD) using artificial intelligence, with a focus on leveraging syn...

Machine learning models for drug-drug interaction prediction from computational discovery to clinical application.

Drug-drug interaction (DDI) poses a major challenge in clinical pharmacology, often compromising therapeutic efficacy or causing serious adverse event...

Synergies between data science methods and innovative drug delivery technologies.

Most areas of science and technology and beyond are undergoing an almost unprecedented rate of change, driven largely by the rapid growth in automatio...

PBPK Modeling Addresses Oral Absorption-Mediated Drug Interactions.

Absorption is the first and imperative step to understanding the pharmacokinetics (PK) and ADME (absorption, distribution, metabolism, and excretion) ...

Distinguishing a drug use disorder from drug use in a high-risk sample of youth: A random forest classification and explanatory analysis.

Although many adolescents and young adults experiment with drugs, a subset may develop a drug use disorder (DUD). Few studies have used machine learni...

Attention-Guided Multiview Deep Learning Framework Uncovers miRNA-Drug Associations for Therapeutic Discovery.

MicroRNAs (miRNAs) play critical roles in regulating various biological processes and offer significant potential for treating human diseases. Aberran...

Mechanistic insights into heart failure progression and therapeutic target discovery through plasma microRNA profiling: Findings from HOMAGE.

BACKGROUND: Identifying early pathobiological mechanisms associated with the onset and progression of heart failure (HF) could guide development of pr...

Human-AI interaction and collaboration in radiology: from conceptual frameworks to responsible implementation.

Artificial intelligence (AI) is entering routine radiology practice, but most studies evaluate algorithms in isolation rather than their interaction w...

The predictive edge: modeling and simulation in drug product development.

It is well-known that drug development is challenging and a time- and resource-intensive endeavor. Historically, it has relied heavily on trial-and-er...

Image-space compensation of inter-crystal scattering in PET using a neural network based filter.

BACKGROUND: In positron emission tomography (PET), gamma photons arriving at the detector ring may undergo one or more Compton scattering events, pote...

Brain aging in bipolar disorder using a neuroimaging and machine learning-derived metric: Findings from the ENIGMA BD Working Group.

BACKGROUND: Bipolar disorder (BD) is associated with clinical and biological markers of premature aging. In this largest study of brain age in BD to d...

Development and temporal validation of machine learning models for predicting clinically relevant medication reconciliation discrepancies at the emergency department: A single-center retrospective study.

OBJECTIVE: Medication discrepancies at hospital admission are common and can cause preventable patient harm. Predictive models can help prioritize med...