Infectious Disease

Latest AI and machine learning research in infectious disease for healthcare professionals.

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Functional immune state classification of unlabeled live human monocytes using holotomography and machine learning

Precise evaluation of immune status is critical for managing diseases such as sepsis, in which the immune system transitions between hyper-inflammatory and immune-suppressed states. However, current biomarkers are limited by low specificity and time-consuming protocols. Here, we present a label-free, imaging-based framework for single-cell immune profiling of human monocytes using three-dimensiona...

Early antifungal resistance prediction based on MALDI-TOF mass spectrometry and machine learning

Antimicrobial resistance (AMR) is a significant global health threat. Recent studies have shown that combining MALDI-TOF mass spectrometry with machine learning (ML) algorithms can accelerate AMR determination. However, these efforts have predominantly focused on bacterial pathogens. Given the significant morbidity, mortality and healthcare costs associated with fungal infections and their evolvin...

ESMDynamic: Fast and Accurate Prediction of Protein Dynamic Contact Maps from Single Sequences

Understanding conformational dynamics is essential for elucidating protein function, yet most deep learning models in structural biology predict only ...

An improved deep learning model for immunogenic B epitope prediction

The recognition of B epitopes by B cells of the immune system initiates an immune response that leads to the production of antibodies to combat bacter...

MoCETSE: A mixture-of-convolutional experts and transformer-based model for predicting Gram-negative bacterial secreted effectors

Identifying effector proteins of Gram-negative bacterial secretion systems is crucial for understanding their pathogenic mechanisms and guiding antimi...

Intra-DNA k-mer Conservation Patterns Encode Evolutionary Selection of Variants

Evolution shapes the structure and content of genomes, yet the contribution of local sequence composition to variant selection remains poorly understo...

DeepHalo: Deep Learning-Powered Exploration of Halogenated Metabolites Uncovering Antibacterial Depsipeptides

In the omics era, confident high-throughput analytical tools are crucial for the efficient identification of metabolites. Here, we present DeepHalo, a...

DeepPROTECTNeo: A Deep learning-based Personalized and RV-guided Optimization tool leveraging TCR Epitope interaction using Context-aware Transformers

The development of personalized cancer vaccines relies heavily on accurately identifying neoepitopes capable of eliciting strong immune responses. T c...

Parvalbumin interneurons mediate spontaneous hemodynamic fluctuations

Resting-state hemodynamic fluctuations are closely linked to gamma-band neural activity, yet the cellular drivers of this neurovascular coupling remai...

Sequence-Dependent Conformational Landscapes of Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) exhibit highly dynamic and heterogeneous conformational ensembles that are strongly influenced by sequence fe...

Identifying pyrogenic contaminants using transcriptomic profiling of monocyte activation test with machine learning

The monocyte activation test is an in vitro pyrogenicity assessment method that can utilise human peripheral blood mononuclear cells to detect pyrogen...

Transfer learning enables discovery of sub-micromolar antibacterials for ESKAPE pathogens from ultra-large chemical spaces

The rise of antimicrobial resistance, especially among gram-negative ESKAPE pathogens, presents an urgent global health threat. However, the discovery...

Variant effect prediction with reliability estimation across priority viruses

Viruses pose a significant threat to global health due to their rapid evolution, adaptability, and increasing potential for cross-species transmission...

Mapping antigenic evolution of influenza A virus using deep learning-based prediction of hemagglutination inhibition titers

Seasonal influenza remains a significant public health challenge through unpredictable antigenic drift, where accumulated mutations enable immune evas...

Non-destructive label-free automated identification of bacterial colonies at the species level directly on agar media using digital holography and convolutional neural network algorithms

this study aimed to develop a fully automated, non-destructive and label-free identification method of bacterial colonies, directly on agar plates, us...

Deep Learning Reveals Endogenous Sterols as Allosteric Modulators of the GPCR-Gα Interface

Endogenous intracellular allosteric modulators of GPCRs remain largely unexplored, with limited binding and phenotype data available. This gap arises ...

Tripleknock: predicting lethal effect of three-gene knockout in bacteria by deep learning

Investigating the lethal effect of multi-gene knockout is essential for discovering novel antibiotics targets and metabolic engineering. Unlike single...

ViralQuest: A user-friendly interactive pipeline for viral-sequences analysis and curation

High-throughput sequencing (HTS) has become an essential, unbiased tool in virology for identifying known and novel viruses. However, analyzing the la...

Reengineering the antigen optimization process for superior neoantigen vaccine design

Identifying effective neoantigen sequences is essential for enhancing anti-tumor immunity. However, the vast sequence space (>109 possible peptides) a...

Generanno: A Genomic Foundation Model for Metagenomic Annotation

The rapid growth of genomic and metagenomic data has underscored the pressing need for advanced computational tools capable of deciphering complex bio...

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