Suggesting disease associations for overlooked metabolites using literature from metabolic neighbours
Delmas, M.; Filangi, O.; Duperier, C.; Paulhe, N.; Vinson, F.; Rodriguez-Mier, P.; Giacomoni, F.; Jourdan, F.; Frainay, C.
Show abstract
In human health research, metabolic signatures extracted from metabolomics data are 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 a large fraction of metabolites are rarely or never mentioned in the literature. Consequently, these overlooked metabolites are often set aside and the interpretation of metabolic signatures is restricted to a subset of the significant metabolites. To suggest potential pathological phenotypes related to these understudied metabolites, we extend the guilt by association principle to literature information by using a Bayesian framework. With this 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.).
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- MetaboListem and TABoLiSTM: Two Deep Learning Algorithms for Metabolite Named Entity Recognition 96%
- Benchmark dataset for training machine learning models to predict the pathway involvement of metabolites 95%
- Atom Identifiers Generated by a Graph Coloring Method Enable Compound Harmonization Across Metabolic Databases 94%
Similar papers in this journal
- FORUM: Building a Knowledge Graph from public databases and scientific literature to extract associations between chemicals and diseases 97%
- MOViDA: Multi-Omics Visible Drug Activity Prediction with a Biologically Informed Neural Network Model 94%
- Strategies for robust, accurate, and generalizable benchmarking of drug discovery platforms 94%
Similar papers in this journal
- Controlling astrocyte-mediated synaptic pruning signals for schizophrenia drug repurposing with Deep Graph Networks 95%
- Genome scale metabolic network modelling for metabolic profile predictions 94%
- Pathway analysis in metabolomics: pitfalls and best practice for the use of over-representation analysis 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.