PlumX Metrics
Embed PlumX Metrics

A deep learning genome-mining strategy for biosynthetic gene cluster prediction

Nucleic Acids Research, ISSN: 1362-4962, Vol: 47, Issue: 18, Page: E110-null
2019
  • 165
    Citations
  • 0
    Usage
  • 395
    Captures
  • 5
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    165
  • Captures
    395
  • Mentions
    5
    • References
      3
      • Wikipedia
        3
    • Blog Mentions
      1
      • Blog
        1
    • News Mentions
      1
      • News
        1

Most Recent News

Translating the code of life into small molecule medicines

Jason Park, Ph.D, CEO & Co-Founder of Empress Therapeutics and Operating Partner, Flagship Pioneering, examines why technologies such as artificial intelligence (AI) and machine learning

Article Description

Natural products represent a rich reservoir of small molecule drug candidates utilized as antimicrobial drugs, anticancer therapies, and immunomodulatory agents. These molecules are microbial secondary metabolites synthesized by co-localized genes termed Biosynthetic Gene Clusters (BGCs). The increase in full microbial genomes and similar resources has led to development of BGC prediction algorithms, although their precision and ability to identify novel BGC classes could be improved. Here we present a deep learning strategy (DeepBGC) that offers reduced false positive rates in BGC identification and an improved ability to extrapolate and identify novel BGC classes compared to existing machine-learning tools. We supplemented this with random forest classifiers that accurately predicted BGC product classes and potential chemical activity. Application of DeepBGC to bacterial genomes uncovered previously undetectable putative BGCs that may code for natural products with novel biologic activities. The improved accuracy and classification ability of DeepBGC represents a major addition to in-silico BGC identification.

Bibliographic Details

Hannigan, Geoffrey D; Prihoda, David; Palicka, Andrej; Soukup, Jindrich; Klempir, Ondrej; Rampula, Lena; Durcak, Jindrich; Wurst, Michael; Kotowski, Jakub; Chang, Dan; Wang, Rurun; Piizzi, Grazia; Temesi, Gergely; Hazuda, Daria J; Woelk, Christopher H; Bitton, Danny A

Oxford University Press (OUP)

Biochemistry, Genetics and Molecular Biology

Provide Feedback

Have ideas for a new metric? Would you like to see something else here?Let us know