IntLIM: integration using linear models of metabolomics and gene expression data

@inproceedings{Siddiqui2018IntLIMIU,
  title={IntLIM: integration using linear models of metabolomics and gene expression data},
  author={Jalal K. Siddiqui and Elizabeth Baskin and Mingrui Liu and Carmen Z. Cantemir-Stone and Bofei Zhang and Russell Bonneville and Joseph P. McElroy and Kevin R. Coombes and Ewy A. Math{\'e}},
  booktitle={BMC Bioinformatics},
  year={2018}
}
  • Jalal K. Siddiqui, Elizabeth Baskin, +6 authors Ewy A. Mathé
  • Published in BMC Bioinformatics 2018
  • Computer Science, Medicine, Biology
  • BackgroundIntegration of transcriptomic and metabolomic data improves functional interpretation of disease-related metabolomic phenotypes, and facilitates discovery of putative metabolite biomarkers and gene targets. For this reason, these data are increasingly collected in large (> 100 participants) cohorts, thereby driving a need for the development of user-friendly and open-source methods/tools for their integration. Of note, clinical/translational studies typically provide snapshot (e.g… CONTINUE READING

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