AIMC Topic: Host-Pathogen Interactions

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Mimicry Embedding Facilitates Advanced Neural Network Training for Image-Based Pathogen Detection.

mSphere
The use of deep neural networks (DNNs) for analysis of complex biomedical images shows great promise but is hampered by a lack of large verified data sets for rapid network evolution. Here, we present a novel strategy, termed "mimicry embedding," for...

Target Identification Using Homopharma and Network-Based Methods for Predicting Compounds Against Dengue Virus-Infected Cells.

Molecules (Basel, Switzerland)
Drug target prediction is an important method for drug discovery and design, can disclose the potential inhibitory effect of active compounds, and is particularly relevant to many diseases that have the potential to kill, such as dengue, but lack any...

A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections.

Nature communications
Improved identification of bacterial and viral infections would reduce morbidity from sepsis, reduce antibiotic overuse, and lower healthcare costs. Here, we develop a generalizable host-gene-expression-based classifier for acute bacterial and viral ...

OHMI: the ontology of host-microbiome interactions.

Journal of biomedical semantics
BACKGROUND: Host-microbiome interactions (HMIs) are critical for the modulation of biological processes and are associated with several diseases. Extensive HMI studies have generated large amounts of data. We propose that the logical representation o...

mSphere of Influence: the Rise of Artificial Intelligence in Infection Biology.

mSphere
Artur Yakimovich works in the field of computational virology and applies machine learning algorithms to study host-pathogen interactions. In this mSphere of Influence article, he reflects on two papers "Holographic Deep Learning for Rapid Optical Sc...

Machine-Learning-Based Predictor of Human-Bacteria Protein-Protein Interactions by Incorporating Comprehensive Host-Network Properties.

Journal of proteome research
The large-scale identification of protein-protein interactions (PPIs) between humans and bacteria remains a crucial step in systematically understanding the underlying molecular mechanisms of bacterial infection. Computational prediction approaches a...

Defining host-pathogen interactions employing an artificial intelligence workflow.

eLife
UNLABELLED: For image-based infection biology, accurate unbiased quantification of host-pathogen interactions is essential, yet often performed manually or using limited enumeration employing simple image analysis algorithms based on image segmentati...

Systems Biology and Machine Learning in Plant-Pathogen Interactions.

Molecular plant-microbe interactions : MPMI
Systems biology is an inclusive approach to study the static and dynamic emergent properties on a global scale by integrating multiomics datasets to establish qualitative and quantitative associations among multiple biological components. With an abu...

A multicentre verification study of the QuantiFERON-TB Gold Plus assay.

Tuberculosis (Edinburgh, Scotland)
OBJECTIVES: The aim of this verification study was to compare the QuantiFERON-TB Gold Plus (QFT-Plus) to the QuantiFERON-TB Gold In Tube (QFT-GIT). The new QFT-Plus test contains an extra antigen tube which, according to the manufacturer additionally...

Three-dimensional visualization and a deep-learning model reveal complex fungal parasite networks in behaviorally manipulated ants.

Proceedings of the National Academy of Sciences of the United States of America
Some microbes possess the ability to adaptively manipulate host behavior. To better understand how such microbial parasites control animal behavior, we examine the cell-level interactions between the species-specific fungal parasite and its carpente...