Vaccines

Enhancing TCR-Peptide Interaction Prediction with Pretrained Language Models and Molecular Representations

Understanding the binding specificity between T-cell receptors (TCRs) and peptide-major histocompatibility complexes (pMHCs) is central to immunotherapy and vaccine development. However, current predictive models struggle with generalization, especially in data-scarce settings and when faced with novel epitopes. We present LANTERN (Large lAnguage model-powered TCR-Enhanced Recognition Network), ...

Graph-Based Prediction Models for Data Debiasing

Bias in data collection, arising from both under-reporting and over-reporting, poses significant challenges in critical applications such as healthcare and public safety. In this work, we introduce Graph-based Over- and Under-reporting Debiasing (GROUD), a novel graph-based optimization framework that debiases reported data by jointly estimating the true incident counts and the associated report...

CEC-MMR: Cross-Entropy Clustering Approach to Multi-Modal Regression

In practical applications of regression analysis, it is not uncommon to encounter a multitude of values for each attribute. In such a situation, the...

Task as Context Prompting for Accurate Medical Symptom Coding Using Large Language Models

Accurate medical symptom coding from unstructured clinical text, such as vaccine safety reports, is a critical task with applications in pharmacovig...

A Scalable Predictive Modelling Approach to Identifying Duplicate Adverse Event Reports for Drugs and Vaccines

The practice of pharmacovigilance relies on large databases of individual case safety reports to detect and evaluate potential new causal associatio...

One Look is Enough: A Novel Seamless Patchwise Refinement for Zero-Shot Monocular Depth Estimation Models on High-Resolution Images

Zero-shot depth estimation (DE) models exhibit strong generalization performance as they are trained on large-scale datasets. However, existing mode...

MMR: A Large-scale Benchmark Dataset for Multi-target and Multi-granularity Reasoning Segmentation

The fusion of Large Language Models with vision models is pioneering new possibilities in user-interactive vision-language tasks. A notable applicat...

COVID 19 Diagnosis Analysis using Transfer Learning

Coronaviruses, including SARS-CoV-2, are responsible for COVID-19, a highly transmissible disease that emerged in December 2019 in Wuhan, China. Dur...

MUSS: Multilevel Subset Selection for Relevance and Diversity

The problem of relevant and diverse subset selection has a wide range of applications, including recommender systems and retrieval-augmented generat...

TAIJI: Textual Anchoring for Immunizing Jailbreak Images in Vision Language Models

Vision Language Models (VLMs) have demonstrated impressive inference capabilities, but remain vulnerable to jailbreak attacks that can induce harmfu...

VaxGuard: A Multi-Generator, Multi-Type, and Multi-Role Dataset for Detecting LLM-Generated Vaccine Misinformation

Recent advancements in Large Language Models (LLMs) have significantly improved text generation capabilities. However, they also present challenges,...

To Vaccinate or not to Vaccinate? Analyzing $\mathbb{X}$ Power over the Pandemic

The COVID-19 pandemic has profoundly affected the normal course of life -- from lock-downs and virtual meetings to the unprecedentedly swift creatio...

A Double Machine Learning Approach for the Evaluation of COVID-19 Vaccine Effectiveness Under the Test-Negative Design: Analysis of Québec Administrative Data.

The test-negative design (TND), which is routinely used for monitoring seasonal flu vaccine effectiveness (VE), has recently become integral to COVID-...

[Construction and preliminary validation of machine learning predictive models for cervical cancer screening based on human DNA methylation].

Using methylation characteristics of human genes to construct machine learning predictive models for screening cervical cancer and precancerous lesio...

Helix-mRNA: A Hybrid Foundation Model For Full Sequence mRNA Therapeutics

mRNA-based vaccines have become a major focus in the pharmaceutical industry. The coding sequence as well as the Untranslated Regions (UTRs) of an m...

Cancer Vaccine Adjuvant Name Recognition from Biomedical Literature using Large Language Models

Motivation: An adjuvant is a chemical incorporated into vaccines that enhances their efficacy by improving the immune response. Identifying adjuvant...

From Individual Experience to Collective Evidence: A Reporting-Based Framework for Identifying Systemic Harms

When an individual reports a negative interaction with some system, how can their personal experience be contextualized within broader patterns of s...

A Fair and Optimal Approach to Sequential Healthcare Rationing

The COVID-19 pandemic underscored the urgent need for fair and effective allocation of scarce resources, from hospital beds to vaccine distribution....

FCBoost-Net: A Generative Network for Synthesizing Multiple Collocated Outfits via Fashion Compatibility Boosting

Outfit generation is a challenging task in the field of fashion technology, in which the aim is to create a collocated set of fashion items that com...

VirusImmu: a novel ensemble machine learning approach for viral immunogenicity prediction.

The viruses threats provoke concerns regarding their sustained epidemic transmission, making the development of vaccines particularly important. In th...