%Aigaion2 BibTeX export from Idiap Publications
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@ARTICLE{Delmas_GIGASCIENCE_2023,
         author = {Delmas, Maxime and Filangi, Olivier and Christophe, Duperier and Nils, Paulhe and Vinson, Florence and Mier, Pablo Rodriguez and Franck, Giacomoni and Jourdan, Fabien and Frainay, Cl{\'{e}}ment},
       keywords = {Metabolic networks},
       projects = {Idiap},
          month = sep,
          title = {Suggesting disease associations for overlooked metabolites using literature from metabolic neighbors},
        journal = {GigaScience},
         volume = {12},
           year = {2023},
          pages = {13},
           issn = {2047-217X},
            doi = {https://doi.org/10.1093/gigascience/giad065},
       abstract = {In human health research, metabolic signatures extracted from metabolomics data have a strong added value for stratifying patients and identifying biomarkers. Nevertheless, one of the main challenges is to interpret and relate these lists of discriminant metabolites to pathological mechanisms. This task requires experts to combine their knowledge with information extracted from databases and the scientific literature. However, we show that most compounds (>99\%) in the PubChem database lack annotated literature. This dearth of available information can have a direct impact on the interpretation of metabolic signatures, which is often restricted to a subset of significant metabolites. To suggest potential pathological phenotypes related to overlooked metabolites that lack annotated literature, we extend the “guilt-by-association” principle to literature information by using a Bayesian framework. The underlying assumption is that the literature associated with the metabolic neighbors of a compound can provide valuable insights, or an a priori, into its biomedical context. The metabolic neighborhood of a compound can be defined from a metabolic network and correspond to metabolites to which it is connected through biochemical reactions. With the proposed approach, we suggest more than 35,000 associations between 1,047 overlooked metabolites and 3,288 diseases (or disease families). All these newly inferred associations are freely available on the FORUM ftp server (see information at https://github.com/eMetaboHUB/Forum-LiteraturePropagation).},
            pdf = {https://publications.idiap.ch/attachments/papers/2023/Delmas_GIGASCIENCE_2023.pdf}
}