Bioterrorism

Scaling up drug combination surface prediction.

Drug combinations are required to treat advanced cancers and other complex diseases. Compared with monotherapy, combination treatments can enhance efficacy and reduce toxicity by lowering the doses of single drugs-and there especially synergistic combinations are of interest. Since drug combination screening experiments are costly and time-consuming, reliable machine learning models are needed for...

CirT: Global Subseasonal-to-Seasonal Forecasting with Geometry-inspired Transformer

Accurate Subseasonal-to-Seasonal (S2S) climate forecasting is pivotal for decision-making including agriculture planning and disaster preparedness but is known to be challenging due to its chaotic nature. Although recent data-driven models have shown promising results, their performance is limited by inadequate consideration of geometric inductive biases. Usually, they treat the spherical weathe...

AI-driven health analysis for emerging respiratory diseases: A case study of Yemen patients using COVID-19 data.

In low-income and resource-limited countries, distinguishing COVID-19 from other respiratory diseases is challenging due to similar symptoms and the p...

Can LLMs Simulate Social Media Engagement? A Study on Action-Guided Response Generation

Social media enables dynamic user engagement with trending topics, and recent research has explored the potential of large language models (LLMs) fo...

AAKT: Enhancing Knowledge Tracing with Alternate Autoregressive Modeling

Knowledge Tracing (KT) aims to predict students' future performances based on their former exercises and additional information in educational setti...

Data-driven Super-Resolution of Flood Inundation Maps using Synthetic Simulations

The frequency of extreme flood events is increasing throughout the world. Daily, high-resolution (30m) Flood Inundation Maps (FIM) observed from spa...

Self-Correcting Decoding with Generative Feedback for Mitigating Hallucinations in Large Vision-Language Models

While recent Large Vision-Language Models (LVLMs) have shown remarkable performance in multi-modal tasks, they are prone to generating hallucinatory...

Social inequality and cultural factors impact the awareness and reaction during the cryptic transmission period of pandemic

The World Health Organization (WHO) declared the COVID-19 outbreak a Public Health Emergency of International Concern (PHEIC) on January 31, 2020. H...

A Novel Zero-Touch, Zero-Trust, AI/ML Enablement Framework for IoT Network Security

The IoT facilitates a connected, intelligent, and sustainable society; therefore, it is imperative to protect the IoT ecosystem. The IoT-based 5G an...

A Modal-Based Approach for System Frequency Response and Frequency Nadir Prediction

This letter introduces a novel approach for predicting system frequency response and frequency nadir by leveraging modal information. It significant...

Non-Linear Dose-Response Relationship for Metformin in Japanese Patients With Type 2 Diabetes: Analysis of Irregular Longitudinal Data by Interpretable Machine Learning Models.

The dose-response relationship between metformin and change in hemoglobin A1c (HbA1c) shows a maximum at 1500-2000 mg/day in patients with type 2 diab...

A Zero-Shot LLM Framework for Automatic Assignment Grading in Higher Education

Automated grading has become an essential tool in education technology due to its ability to efficiently assess large volumes of student work, provi...

BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response

Disaster events occur around the world and cause significant damage to human life and property. Earth observation (EO) data enables rapid and compre...

Motif Discovery Framework for Psychiatric EEG Data Classification

In current medical practice, patients undergoing depression treatment must wait four to six weeks before a clinician can assess medication response ...

Multi-Agent Conversational Online Learning for Adaptive LLM Response Identification

The remarkable generative capability of large language models (LLMs) has sparked a growing interest in automatically generating responses for differ...

Multimodal Benchmarking of Foundation Model Representations for Cellular Perturbation Response Prediction

The decreasing cost of single-cell RNA sequencing (scRNA-seq) has enabled the collection of massive scRNA-seq datasets, which are now being used to tr...

Kinetic parameter prediction using neural networks identifies limitations to C4 photosynthesis

Large-scale kinetic models of photosynthesis enable time-resolved predictions of traits related to this key process, and provide the means to identify...

Novel Computational Pipeline to Identify Target Sites for Broad Spectrum Antiviral Drugs

Emerging viruses pose an ongoing threat to human health. While certain viral families are common sources of outbreaks, predicting the specific virus w...

Enhanced prediction of breast cancer patient response to chemotherapy by integrating deconvolved expression patterns of immune, stromal and tumor cells

The tumor microenvironment (TME) is a complex ecosystem of diverse cell types whose interactions govern tumor growth and clinical outcome. While multi...

WaveSeekerNet: Accurate Prediction of Influenza A Virus Subtypes and Host Source Using Attention-Based Deep Learning

Influenza A virus (IAV) poses a significant threat to animal health globally, with its ability to overcome species barriers and cause pandemics. Rapid...