Prescriptions

Interaction Field Matching: Overcoming Limitations of Electrostatic Models

Electrostatic field matching (EFM) has recently appeared as a novel physics-inspired paradigm for data generation and transfer using the idea of an electric capacitor. However, it requires modeling electrostatic fields using neural networks, which is non-trivial because of the necessity to take into account the complex field outside the capacitor plates. In this paper, we propose Interaction Fie...

Low-Rank Head Avatar Personalization with Registers

We introduce a novel method for low-rank personalization of a generic model for head avatar generation. Prior work proposes generic models that achieve high-quality face animation by leveraging large-scale datasets of multiple identities. However, such generic models usually fail to synthesize unique identity-specific details, since they learn a general domain prior. To adapt to specific subject...

HOSIG: Full-Body Human-Object-Scene Interaction Generation with Hierarchical Scene Perception

Generating high-fidelity full-body human interactions with dynamic objects and static scenes remains a critical challenge in computer graphics and a...

MUDI: A Multimodal Biomedical Dataset for Understanding Pharmacodynamic Drug-Drug Interactions

Understanding the interaction between different drugs (drug-drug interaction or DDI) is critical for ensuring patient safety and optimizing therapeu...

MTCMB: A Multi-Task Benchmark Framework for Evaluating LLMs on Knowledge, Reasoning, and Safety in Traditional Chinese Medicine

Traditional Chinese Medicine (TCM) is a holistic medical system with millennia of accumulated clinical experience, playing a vital role in global he...

A deep learning-based method for predicting the frequency classes of drug side effects based on multi-source similarity fusion.

MOTIVATION: Drug side effects refer to harmful or adverse reactions that occur during drug use, unrelated to the therapeutic purpose. A core issue in ...

Comparative analysis of computational approaches for predicting Transthyretin transcription activators and human dopamine D1 receptor antagonists

The study expands the application of scikit-learn-based machine learning (ML) to the prediction of small biomolecule functionalities based on Carbon...

Simple Prompt Injection Attacks Can Leak Personal Data Observed by LLM Agents During Task Execution

Previous benchmarks on prompt injection in large language models (LLMs) have primarily focused on generic tasks and attacks, offering limited insigh...

Advancements in Organoid-Based Drug Discovery: Revolutionizing Precision Medicine and Pharmacology.

Organoids, 3D cellular models derived from stem cells, have revolutionized drug testing by providing human-relevant systems for modeling diseases and ...

Drug-Drug interactions and special considerations in breast cancer patients treated with CDK4/6 inhibitors: A comprehensive review.

Cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) have reshaped the treatment paradigm of hormone receptor positive (HR + )/HER2-negative breast cancer...

Use of an Untrained Large Language Model for Antibiotic Prescription in Pediatric Infectious Diseases at Primary Care Settings: A Study From the Italian Society for Pediatric Infectious Diseases.

The development of artificial intelligence systems is revolutionizing many fields of medicine, but specific studies are still missing in pediatrics. I...

MCD-LightGBM System for Intelligent Analyzing Heterogeneous Clinical Drug Therapeutic Effects.

Causal effect estimation of individual heterogeneity is a core issue in the field of causal inference, and its application in medicine poses an active...

TriCvT-DTI: Predicting Drug-Target Interactions Using Trimodal Representations and Convolutional Vision Transformers.

Predicting interactions between drugs and their targets is vital for drug discovery and repositioning. Conventional techniques are slow and labor-inte...

Optimized Drug-Drug Interaction Extraction With BioGPT and Focal Loss-Based Attention.

Drug-drug interactions (DDIs) are a significant focus in biomedical research and clinical practice due to their potential to compromise treatment outc...

PregAN-NET: Addressing Class Imbalance with GANs in Interpretable Computational Framework for Predicting Safety Profile of Drugs Considering Adverse Reactions During Pregnancy.

Adverse Drug Reactions (ADRs) during pregnancy pose significant risks to both the mother and the fetus. Conventional approaches to predict ADR are ina...

MiRAGE-DTI: A novel approach for drug-target interaction prediction by integrating drug and target similarity metrics.

MOTIVATION: Accurately predicting drug-target interactions (DTIs) is critical for accelerating drug discovery, repositioning, and development. Traditi...

Construction and Validation of Artificial Neural Network Model Suggesting Nursing Diagnosis: A Proof-of-Concept Study.

There are challenges involving human resource management, as the selection and evaluation processes for nursing diagnostic labels are time-consuming, ...

Deep learning driven interpretable and informed decision making model for brain tumour prediction using explainable AI.

Brain Tumours are highly complex, particularly when it comes to their initial and accurate diagnosis, as this determines patient prognosis. Convention...

A novel in silico approach for predicting unbound brain-to-plasma ratio using machine learning-based support vector regression.

The blood-brain barrier (BBB) functions as a vital protective mechanism, restricting the entry of substances and xenobiotics into the central nervous ...

Neuroadaptive Admittance Control for Human-Robot Interaction With Human Motion Intention Estimation and Output Error Constraint.

Human-robot interaction (HRI) is a crucial component in the field of robotics, and enabling faster response, higher accuracy, as well as smaller human...