ArticleFrontiers in microbiology2019
Prediction of T4SS Effector Proteins for
Article in Frontiers in microbiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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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.
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Who cites it
21 citing papers in PubMed, 29 citations in OpenAlex.
- GeoEPred: A multimodal structure-aware geometric deep learning framework for Gram-negative bacterial secreted effector prediction with sequence semantics.PLoS computational biology · 2026Article
- Adaptation ofmSphere · 2026Article
- An Nrf2-Nup214 positive feedback loop sustains the antioxidant response and promotes microbial infection in ticks.bioRxiv : the preprint server for biology · 2026Article
- TXSelect: A multi-task learning model to identify secretory effectors.PLoS computational biology · 2025Article
- Behavioral manipulation ofmBio · 2025Article
- Comparative analysis of Legionella lytica genome identifies specific metabolic traits and virulence factors.Scientific reports · 2025Article
- T4SEpp: A pipeline integrating protein language models to predict bacterial type IV secreted effectors.Computational and structural biotechnology journal · 2024Article
- T4Seeker: a hybrid model for type IV secretion effectors identification.BMC biology · 2024Article
- Cell-selective proteomics reveal novel effectors secreted by an obligate intracellular bacterial pathogen.Nature communications · 2024Article
- Cell-selective proteomics reveal novel effectors secreted by an obligate intracellular bacterial pathogen.bioRxiv : the preprint server for biology · 2023Article
- Analysis of the Type 4 Effectome across the GenusInternational journal of molecular sciences · 2022Article
- Biostatistical prediction of genes essential for growth of Anaplasma phagocytophilum in a human promyelocytic cell line using a random transposon mutant library.Pathogens and disease · 2021Article
- Cells within cells: Rickettsiales and the obligate intracellular bacterial lifestyle.Nature reviews. Microbiology · 2021Review
- iT4SE-EP: Accurate Identification of Bacterial Type IV Secreted Effectors by Exploring Evolutionary Features from Two PSI-BLAST Profiles.Molecules (Basel, Switzerland) · 2021Article
- The enigmatic biology of rickettsiae: recent advances, open questions and outlook.Pathogens and disease · 2021Review
- Ticks: More Than Just a Pathogen Delivery Service.Frontiers in cellular and infection microbiology · 2021Article
- Computational prediction of secreted proteins in gram-negative bacteria.Computational and structural biotechnology journal · 2021Review
- DeepT3_4: A Hybrid Deep Neural Network Model for the Distinction Between Bacterial Type III and IV Secreted Effectors.Frontiers in microbiology · 2021Article
- Global Transcription Profiles ofFrontiers in veterinary science · 2020Article
- Assessment of vector-host-pathogen relationships using data mining and machine learning.Computational and structural biotechnology journal · 2020Review
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 at 1 institution in 1 country.
Funding
Abstract
Type IV secretion systems (T4SS) are used by a number of bacterial pathogens to attack the host cell. The complex protein structure of the T4SS is used to directly translocate effector proteins into host cells, often causing fatal diseases in humans and animals. Identification of effector proteins is the first step in understanding how they function to cause virulence and pathogenicity. Accurate prediction of effector proteins via a machine learning approach can assist in the process of their identification. The main goal of this study is to predict a set of candidate effectors for the tick-borne pathogen
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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.