ReviewFrontiers in microbiology2015
Computational approaches for prediction of pathogen-host protein-protein interactions.
Review in Frontiers in microbiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
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
47 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Harnessing AI for advancing pathogenic microbiology: a bibliometric and topic modeling approach.Frontiers in microbiology · 2024Pooled it
- Omics Approaches in Hantavirus Research: Current Advances, Challenges, and Future Perspectives.Biotech (Basel (Switzerland)) · 2026Review
- Optimizing computational prediction of shrimp-WSSV protein interactions through integration of homology- and topology-based predictions.Molecular genetics and genomics : MGG · 2026Article
- PPILS: Protein-protein interaction prediction with language of biological coding.Computers in biology and medicine · 2025Article
- mimicINT: A workflow for microbe-host protein interaction inference.F1000Research · 2025Article
- Computational Approach for Detection and Analysis of Human-Virus Protein-Protein Interactions.Methods in molecular biology (Clifton, N.J.) · 2025Article
- In silico exploration of hypothetical proteins inIn silico pharmacology · 2024Article
- Prediction of mammalian virus cross-species transmission based on host proteins.Microbiology spectrum · 2023Article
- A robust protein language model for SARS-CoV-2 protein-protein interaction network prediction.Artificial intelligence in medicine · 2023Article
- PPIntegrator: semantic integrative system for protein-protein interaction and application for host-pathogen datasets.Bioinformatics advances · 2023Article
- In silico functional and structural characterization revealed virulent proteins ofMolecular biology research communications · 2022Article
- Article
- Deciphering the Host-Pathogen Interactome of the Wheat-Common Bunt System: A Step towards Enhanced Resilience in Next Generation Wheat.International journal of molecular sciences · 2022Article
- Deciphering the Interactions of SARS-CoV-2 Proteins with Human Ion Channels Using Machine-Learning-Based Methods.Pathogens (Basel, Switzerland) · 2022Article
- In silico prediction of HIV-1-host molecular interactions and their directionality.PLoS computational biology · 2022Article
- Deciphering the Crosstalk Mechanisms of Wheat-Stem Rust Pathosystem: Genome-Scale Prediction Unravels Novel Host Targets.Frontiers in plant science · 2022Article
- Computational models for prediction of protein-protein interaction in rice andFrontiers in plant science · 2022Article
- Host-pathogen protein-nucleic acid interactions: A comprehensive review.Computational and structural biotechnology journal · 2022Review
- Machine Learning and Its Applications for Protozoal Pathogens and Protozoal Infectious Diseases.Frontiers in cellular and infection microbiology · 2022Review
- Identification of viral-mediated pathogenic mechanisms in neurodegenerative diseases using network-based approaches.Briefings in bioinformatics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Infectious diseases are still among the major and prevalent health problems, mostly because of the drug resistance of novel variants of pathogens. Molecular interactions between pathogens and their hosts are the key parts of the infection mechanisms. Novel antimicrobial therapeutics to fight drug resistance is only possible in case of a thorough understanding of pathogen-host interaction (PHI) systems. Existing databases, which contain experimentally verified PHI data, suffer from scarcity of reported interactions due to the technically challenging and time consuming process of experiments. These have motivated many researchers to address the problem by proposing computational approaches for analysis and prediction of PHIs. The computational methods primarily utilize sequence information, protein structure and known interactions. Classic machine learning techniques are used when there are sufficient known interactions to be used as training data. On the opposite case, transfer and multitask learning methods are preferred. Here, we present an overview of these computational approaches for predicting PHI systems, discussing their weakness and abilities, with future directions.
Indexed as
Identifiers
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.