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Authors and Citation

Authors

  • Edi Prifti. Maintainer.

Citation

Prifti E, Chevaleyre Y, Hanczar B, Belda E, Danchin A, Clément K, Zucker J (2020). “Interpretable and accurate prediction models for metagenomics data.” GigaScience, 9(3), giaa010. ISSN 2047-217X, doi:10.1093/gigascience/giaa010, https://doi.org/10.1093/gigascience/giaa010. 

@Article{,
  title = {Interpretable and accurate prediction models for metagenomics data},
  author = {Edi Prifti and Yann Chevaleyre and Blaise Hanczar and Eugeni Belda and Antoine Danchin and Karine Clément and Jean-Daniel Zucker},
  journal = {GigaScience},
  year = {2020},
  volume = {9},
  number = {3},
  pages = {giaa010},
  month = {March},
  issn = {2047-217X},
  doi = {10.1093/gigascience/giaa010},
  url = {https://doi.org/10.1093/gigascience/giaa010},
  abstract = {Background: Microbiome biomarker discovery for patient diagnosis, 
  prognosis, and risk evaluation is attracting broad interest. Selected groups 
  of microbial features provide signatures that characterize host disease states 
  such as cancer or cardio-metabolic diseases. Yet, the current predictive 
  models stemming from machine learning still behave as black boxes and seldom 
  generalize well. Their interpretation is challenging for physicians and 
  biologists, which makes them difficult to trust and use routinely in the 
  physician–patient decision-making process. Novel methods that provide 
  interpretability and biological insight are needed. Here, we introduce 
  “predomics,” an original machine learning approach inspired by microbial 
  ecosystem interactions that is tailored for metagenomics data. It discovers 
  accurate predictive signatures and provides unprecedented interpretability. 
  The decision provided by the predictive model is based on a simple, yet 
  powerful score computed by adding, subtracting, or dividing cumulative 
  abundance of microbiome measurements. Results: Tested on >100 datasets, we 
  demonstrate that predomics models are simple and highly interpretable. Even 
  with such simplicity, they are at least as accurate as state-of-the-art 
  methods. The family of best models, discovered during the learning process, 
  offers the ability to distil biological information and to decipher the 
  predictability signatures of the studied condition. In a proof-of-concept 
  experiment, we successfully predicted body corpulence and metabolic 
  improvement after bariatric surgery using pre-surgery microbiome data. 
  Conclusions: Predomics is a new algorithm that helps in providing reliable 
  and trustworthy diagnostic decisions in the microbiome field. Predomics is 
  in accord with societal and legal requirements that plead for an explainable 
  artificial intelligence approach in the medical field.},
}