Evidence map›Paper›PMID 35215934›Full record

ArticleViruses2022

PhageLeads: Rapid Assessment of Phage Therapeutic Suitability Using an Ensemble Machine Learning Approach.

Kumarasan Yukgehnaish, Heera Rajandas, Sivachandran Parimannan, Ravichandran Manickam, Kasi Marimuthu, Bent Petersen, Martha R J Clokie, Andrew Millard, Thomas Sicheritz-Pontén

Open access · goldAbstract read
In one paragraph

Article in Viruses, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
100citing papers in PubMed, 1 pooled it
21.9field-weighted citation impact, top 1% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

100 citing papers in PubMed, 1 synthesis or guideline pooled it, 127 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Isolation and characterization of two phages infectingThe Journal of general virology · 2026
    Article
  5. Article
  6. Article
  7. Genome sequence ofMicrobiology resource announcements · 2026
    Article
  8. Article
  9. Genomic landscape of 340 virulent Acinetobacter bacteriophages reveals anti-CRISPR-enriched candidates for therapeutic prioritization.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026
    Article
  10. Article
  11. Article
  12. Article
  13. Genome characterization ofMicrobiology resource announcements · 2026
    Article
  14. Isolation and characterisation of a novel Stenotrophomonas maltophilia phage vB_SmaS_BCU-1 with evaluation of mammalian cell safety.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026
    Article
  15. Article
  16. Antibacterial Efficacy ofPathogens (Basel, Switzerland) · 2026
    Article
  17. Rolling outMicrobial genomics · 2026
    Article
  18. Isolation and Characterization of aAntibiotics (Basel, Switzerland) · 2026
    Article
  19. Article
  20. Review

40 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 3 countries.

Kumarasan YukgehnaishCenter of Excellence for Omics-Driven Computational Biodiscovery (COMBio), AIMST University, Bedong 08100, Kedah, Malaysia.
Heera RajandasCenter of Excellence for Omics-Driven Computational Biodiscovery (COMBio), AIMST University, Bedong 08100, Kedah, Malaysia.
Sivachandran ParimannanCenter of Excellence for Omics-Driven Computational Biodiscovery (COMBio), AIMST University, Bedong 08100, Kedah, Malaysia.ORCID 0000-0002-0101-1858
Ravichandran ManickamCenter of Excellence for Omics-Driven Computational Biodiscovery (COMBio), AIMST University, Bedong 08100, Kedah, Malaysia.
Kasi MarimuthuCenter of Excellence for Omics-Driven Computational Biodiscovery (COMBio), AIMST University, Bedong 08100, Kedah, Malaysia.
Bent PetersenCenter of Excellence for Omics-Driven Computational Biodiscovery (COMBio), AIMST University, Bedong 08100, Kedah, Malaysia.ORCID 0000-0002-2472-8317
Martha R J ClokieDepartment Genetics and Genome Biology, University of Leicester, Leicester LE1 7RH, UK.
Andrew MillardDepartment Genetics and Genome Biology, University of Leicester, Leicester LE1 7RH, UK.ORCID 0000-0002-3895-2854
Thomas Sicheritz-PonténCenter of Excellence for Omics-Driven Computational Biodiscovery (COMBio), AIMST University, Bedong 08100, Kedah, Malaysia.
University of Copenhagen · DKAIMST University · MYUniversity of Leicester · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The characterization of therapeutic phage genomes plays a crucial role in the success rate of phage therapies. There are three checkpoints that need to be examined for the selection of phage candidates, namely, the presence of temperate markers, antimicrobial resistance (AMR) genes, and virulence genes. However, currently, no single-step tools are available for this purpose. Hence, we have developed a tool capable of checking all three conditions required for the selection of suitable therapeutic phage candidates. This tool consists of an ensemble of machine-learning-based predictors for determining the presence of temperate markers (integrase, Cro/CI repressor, immunity repressor, DNA partitioning protein A, and antirepressor) along with the integration of the ABRicate tool to determine the presence of antibiotic resistance genes and virulence genes. Using the biological features of the temperate markers, we were able to predict the presence of the temperate markers with high MCC scores (>0.70), corresponding to the lifestyle of the phages with an accuracy of 96.5%. Additionally, the screening of 183 lytic phage genomes revealed that six phages were found to contain AMR or virulence genes, showing that not all lytic phages are suitable to be used for therapy. The suite of predictors, PhageLeads, along with the integrated ABRicate tool, can be accessed online for in silico selection of suitable therapeutic phage candidates from single genome or metagenomic contigs.

Indexed as

Machine LearningPhage TherapyBacteriaBacterial InfectionsBacterial Physiological PhenomenaBacteriophagesGenome, ViralHumansLysogenyViral ProteinsViral ProteinsAMRgenomicslysogenymachine learningphage therapy

Identifiers

PMID35215934
PMCPMC8879740
OpenAlexW4211094053

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.