An image analysis-aided method for redundancy reduction in differentiation of identical Actinobacterial strains

Hedieh Sajedi, Fatemeh Mohammadipanah*, Hamed Kazemi Shariat Panahi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Aim: To simplify the recognition of Actinobacteria, at different stages of the growth phase, from a mixed culture to facilitate the isolation of novel strains of these bacteria for drug discovery purposes. Materials & methods: A method was developed on Gabor transform, and machine learning using k-Nearest Neighbors and Naive Bayes classifier, Logitboost, Bagging and Random Forest to automatically categorize the colonies. Results: A signature pattern was inferred by the model, making the differentiation of identical strains possible. Additionally, higher performance, compared with other classification methods was achieved. Conclusion: This automated approach can contribute to the acceleration of the drug discovery process while it simultaneously can diminish the loss of budget due to the redundancy occurred by the inexperienced researchers.

Original languageEnglish
Pages (from-to)313-329
Number of pages17
JournalFuture Microbiology
Volume13
Issue number3
DOIs
Publication statusPublished - 1 Mar 2018
Externally publishedYes

Keywords

  • Actinobacteria
  • classification
  • colony pattern
  • drug discovery
  • Gabor transform
  • high-throughput isolation

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