Prescriptions

An AI-Native Biofoundry for Autonomous Enzyme Engineering: Integrating Active Learning with Automated Experimentation

The engineering of enzymes with novel functions is a cornerstone of synthetic biology but remains bottlenecked by the fragmentation between computational design and physical execution. While "self-driving" laboratories promise to resolve this, existing systems often rely on rigid, device-specific scripts that lack the flexibility to handle complex, evolving scientific tasks. Here, we report an AI-...

AI-enhanced cardiac digital twins extend drug proarrhythmic risk assessment through experimental data uncertainty propagation and overdose exploration: a loperamide case study

Drug-induced QT interval prolongation is a key biomarker of proarrhythmic risk and central to drug cardiac safety evaluation alongside in vitro assays and animal studies. Current preclinical frameworks, however, provide limited insight into how experimental uncertainty and extreme exposures translate into real-world arrhythmic risk, despite both factors critically modulating outcomes. To address t...

Agentomics: An Agentic System that Autonomously Develops Novel State-of-the-art Solutions for Biomedical Machine Learning Tasks

Motivation: Extracting knowledge from biomedical data is crucial for advancing our understanding of biological systems and developing novel therapeuti...

BEACON: predicting side effects and therapeutics outcomes to drugs by Bridging knowlEdge grAph with CONtextual language model

Biomedical knowledge graphs encode millions of relationships between drugs, proteins, pathways, and diseases, yet translating this structured knowledg...

User-Adaptive Meta-Learning for Cold-Start Medication Recommendation with Uncertainty Filtering

Large-scale Electronic Health Record (EHR) databases have become indispensable in supporting clinical decision-making through data-driven treatment re...

On Safer Reinforcement Learning Policies for Sedation and Analgesia in Intensive Care

Pain management in intensive care usually involves complex trade-offs between therapeutic goals and patient safety, since both inadequate and excessiv...

An Artificial Intelligence-based framework for protein interaction design with accelerated KAN-based Positive-Unlabeled learning

Protein design seeks optimal amino acid sequences for target structures, but designing stable protein complexes remains challenging. We introduce a pr...

Do VLMs Perceive or Recall? Probing Visual Perception vs. Memory with Classic Visual Illusions

Large Vision-Language Models (VLMs) often answer classic visual illusions "correctly" on original images, yet persist with the same responses when ill...

An Implantable Device that Converses with Patients and Learns to Co-Manage Epilepsy

One-third of the world's 70 million people with epilepsy have seizures that are not controlled by medication; and implantable devices are an exciting ...

A brain dynamic model based on graph neural network reflect the inter-region interaction of cortical areas

A central objective in neuroscience is to elucidate how the brain generates complex dynamic activity through the interactions of brain areas. In this ...

Dual-channel graph learning reveals similarity and complementarity in protein-protein interaction networks

Protein-protein interactions (PPIs) are governed by two fundamental interfacial mechanisms: similarity-driven, often involving symmetric structural mo...

Explainable Artificial Intelligence Reveals Potential Candidate Mechanism of Strain-Specific Drug Depletion

Oral medications can be bioaccumulated or metabolised by gastrointestinal bacteria in a process collectively termed drug depletion. The precise biolog...

VISTA-PATH: An interactive foundation model for pathology image segmentation and quantitative analysis in computational pathology

Accurate semantic segmentation for histopathology image is crucial for quantitative tissue analysis and downstream clinical modeling. Recent segmentat...

White-Box mHC: Electromagnetic Spectrum-Aware and Interpretable Stream Interactions for Hyperspectral Image Classification

In hyperspectral image classification (HSIC), most deep learning models rely on opaque spectral-spatial feature mixing, limiting their interpretabilit...

Rethinking Drug-Drug Interaction Modeling as Generalizable Relation Learning

Drug-drug interaction (DDI) prediction is central to drug discovery and clinical development, particularly in the context of increasingly prevalent po...

RAGulate: Retrieval-Augmented Generation for Post-hoc Literature-Grounded Regulatory Assessment

Prioritization of transcription factor (TF)-target relationships predicted by computational models for experimental validation often requires biologis...

DiSPA: Differential Substructure-Pathway Attention for Drug Response Prediction

Accurate prediction of drug response in precision medicine requires models that capture how specific chemical substructures interact with cellular pat...

Pharmacogenomic Determinants of Post-Liver Transplant Diabetes Mellitus: A Systematic Review and In Silico Pharmacogenomic Analysis

Introduction: Tacrolimus remains central to liver transplantation, yet its narrow therapeutic index and pharmacokinetic variability are associated wit...

dgiLIT: A Method for Prioritization and AI Curation of Drug-Gene Interactions

IMPORTANCE: The Drug-Gene Interaction Database (DGIdb) has a long history of driving hypothesis generation for biomedical research through the careful...

Morphology-Driven Inference of Patient-Specific Pathophysiological States Enables Precision Treatment in Chronic Spontaneous Urticaria

Skin diseases manifest as visually observable eruption patterns, making image-based assessment a central component of dermatological diagnosis. While ...