ArticleeLife2019
Defining host-pathogen interactions employing an artificial intelligence workflow.
Article in eLife, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
49 citing papers in PubMed.
- Review
- Artificial intelligence-powered microscopy: Transforming the landscape of parasitology.Journal of microscopy · 2026Review
- Comprehensive diagnostic approaches to feline toxoplasmosis: Bridging traditional methods and emerging technologies.Virulence · 2025Review
- Credible inferences in microbiome research: ensuring rigour, reproducibility and relevance in the era of AI.Nature reviews. Gastroenterology & hepatology · 2025Review
- Article
- Capillary Electrophoresis Electrospray Ionization Mass Spectrometry Reveals Metabolic Perturbations During Nematode Infection inMolecules (Basel, Switzerland) · 2025Article
- Uncovering the potential virulence factors of emerging pathogens using AI/ML-based tools: a case study inFrontiers in cellular and infection microbiology · 2025Article
- Article
- Emerging and reemerging infectious diseases: global trends and new strategies for their prevention and control.Signal transduction and targeted therapy · 2024Review
- A predictive approach for host-pathogen interactions using deep learning and protein sequences.Virusdisease · 2024Article
- Article
- Structural insights into the activation mechanism of antimicrobial GBP1.The EMBO journal · 2024Article
- Community-developed checklists for publishing images and image analyses.Nature methods · 2024Review
- IFNs in host defence and parasite immune evasion duringFrontiers in immunology · 2024Review
- PIM1 controls GBP1 activity to limit self-damage and to guard against pathogen infection.Science (New York, N.Y.) · 2023Article
- Article
- High-content imaging as a tool to quantify and characterize malaria parasites.Cell reports methods · 2023Article
- Microbial Pathogenesis in the Era of Spatial Omics.Infection and immunity · 2023Review
- Leveraging artificial intelligence in the fight against infectious diseases.Science (New York, N.Y.) · 2023Review
- A splitCas9 phenotypic screen in Toxoplasma gondii identifies proteins involved in host cell egress and invasion.Nature microbiology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
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 segmentation. Host protein recruitment to pathogens is often refractory to accurate automated assessment due to its heterogeneous nature. An intuitive intelligent image analysis program to assess host protein recruitment within general cellular pathogen defense is lacking. We present HRMAn (Host Response to Microbe Analysis), an open-source image analysis platform based on machine learning algorithms and deep learning. We show that HRMAn has the capacity to learn phenotypes from the data, without relying on researcher-based assumptions. Using Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.