Latest AI and machine learning research in infectious disease for healthcare professionals.
Despite the success of direct-acting antivirals, preventing hepatitis C virus (HCV) reinfection remains a critical global challenge. To address this, we leveraged deep learning-based de novo protein design to engineer mini-proteins targeting the large extracellular loop (LEL) of the HCV co-receptor CD81. These mini-proteins are predicted to precisely dock into CD81-LEL, occluding the critical bind...
Vaccination is one of the most effective public health interventions. However, vaccine efficacy varies widely among individuals, as immunity arises from complex interplay between genetic, pathogen, and immunological factors. To date, most systems vaccinology studies have remained pathogen-specific, precluding the discovery of potential shared immune architectures underlying durable antibody respon...
Motivation: Identifying bacterial antimicrobial resistance (AMR) is critical for diagnostics and treatment, but resistance is a complex trait arising ...
Low-count Positron Emission Tomography (PET) reconstruction is severely hindered by the dissipative nature of prevailing generative models, where the ...
Background. Genomic foundation models can dramatically accelerate biological research by learning general-purpose representations of genomic data that...
Blood-based biomarkers discovered by machine learning often lack disease specificity and cross-population robustness for clinical applications. We des...
Background. Clostridioides difficile infection (CDI) imposes a burden that extends well beyond the gastrointestinal tract, yet existing outcome measur...
Diarrheal disease remains a significant cause of morbidity and mortality in children under five years of age in low and middle-income countries. Ident...
High-throughput transcriptomics has transformed disease biology, but its outputs often remain fragmented into gene and pathway lists that are difficul...
Abstract Climate change is altering environmental conditions that influence foodborne disease transmission, yet traditional systematic reviews cannot ...
Motivation: Antimicrobial resistance (AMR) has been identified as a top global public health threat. Accurate AMR phenotype prediction from whole-geno...
Deep learning image classifiers achieve strong predictive performance yet remain opaque in how decisions are formed. A model may predict correctly whi...
Public microbial genomes encode an immense record of biological diversity, evolution and molecular function, but much of this information remains diff...
Accurate quantification of lung disease severity from chest imaging is critical for clinical decision-making and resource allocation. We propose a tri...
Introduction: Plasma metagenomic next-generation sequencing (mNGS) may detect pathogens in solid organ transplant (SOT) recipients, but optimal patien...
Background Early outbreak detection often depends on complex, data-intensive models that have limited operational use in sparse surveillance settings....
Machine learning (ML) has accelerated molecular discovery, yet training models to generalize to out-of-distribution (OOD) chemical spaces remains fund...
Background: Tuberculosis, especially drug-resistant tuberculosis (DR-TB) including multidrug-resistant (MDR) and extensively drug-resistant (XDR) stra...
Predicting phenotype from genotype in extant organisms is increasingly tractable through the accumulation of genome sequences and the development of m...
Advancements in immunogenomics and immuno-oncology have enabled the development of personalized cancer vaccines (PCVs) that target cancer cell-specifi...