Publications

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Publications

  1. Rocca-Serra, P., Salek, R.M., Arita, M., Correa, E., Dayalan, S., Gonzalez-Beltran A., Ebbels T. et al. (2016) Data standards can boost metabolomics research, and if there is a will, there is a way. Metabolomics, 12(1):14. DOI: 10.1007/s11306-015-0879-3
  2. Merlet, B., Paulhe N., Vinson, F., Frainay, C., Chazalvie,l M., Poupin, N., Gloaguen, Y. et al. (2016) A Computational Solution to Automatically Map Metabolite Libraries in the Context of Genome Scale MetabolicNetworksFront Mol Biosci., Feb 16; 3:2. DOI:  10.3389/fmolb.2016.00002
  3. Blaise, B.J., Correia, G., Tin, A., Young, J.H., Vergnaud, A.C., Lewis, M., Pearce, J.T.  et al. (2016) Power Analysis and Sample Size Determination in Metabolic Phenotyping. Anal Chem. May 17; 88(10): 5179-88. DOI:10.1021/acs.analchem.6b00188
  4. Saccenti, E., Menichetti, G., Ghini, V., Remondini, D., Tenori, L., and Luchinat, C. (2016) EntropyBased Network Representation of the Individual Metabolic PhenotypeJ Proteome Res. Sep 2; 15(9): 3298-307. DOI:10.1021/acs.jproteome.6b00454
  5. Karaman, I., Ferreira, D.L., Boulange, C.L., Kaluarachchi, M.R., Herrington, D., Dona, A.C, Castagné, R. et al. (2016) A Workflow For Integrated Processing of Multi-Cohort Untargeted 1H NMR Metabolomics Data In Large ScaleMetabolic EpidemiologyJ Proteome Res. 2016 Sep 15. DOI: 10.1021/acs.jproteome.6b00125
  6. Bandrowski, A., Brinkman, R., Brochhausen, M., Brush, M.H, Bug, B., Chibucos, M.C., Clancy, K. et al. The Ontology for Biomedical Investigations. PLoS One. 2016 Apr 29;11(4):e0154556. DOI:10.1371/journal.pone.0154556
  7. Selivanov, V.A., Benito, A., Miranda, A., Aguilar, E., Polat I.H., Centelles, J.J., Jayaraman, A., Lee, P.W., Marin, S. and Cascante, M. MIDcor, an R-program for deciphering mass interferences in mass spectra of metabolites enriched in stable isotopes. BMC Bioinformatics. 2017. DOI: 10.1186/s12859-017-1513-3
  8. Haug, K, Salek, R.M. and Steinbeck, C. Global open data management in metabolomicsCurr Opin Chem Biol.2017. DOI: 10.1016/j.cbpa.2016.12.024
  9. Takis, P.G., Tenori, L., Ravera, E. and Luchinat, C. Gelified Biofluids for High-Resolution Magic Angle Spinning 1H NMR Analysis: The Case of Urine. Anal Chem. 2017 89(2):1054-1058.DOI:10.1021/acs.analchem.6b04318
  10. Cacciatore, S., Tenori, L., Luchinat, C., Bennett, P.R and MacIntyre, D.A. KODAMA: an R package for knowledge discovery and data miningBioinformatics. 2017 15;33(4):621-623. DOI:10.1093/bioinformatics/btw705

Newsletter, Articles and News

  1. Namrata Kale and Christoph Steinbeck. PhenoMeNal: towards an e-Infrastructure for pheno- and genotyping data. EGI (2015).
  2. Namrata Kale, Christoph Steinbeck and PhenoMeNal Consortium. PhenoMeNal—An e-infrastructure for analysis of metabolic phenotype data. Metabolomics Spotlight, MetaboNews (January, 2016).
  3. EMBL-EBI Press release – Phenomenal: a gateway to personalised medicine (September 2015)
  4. DTL – PhenoMeNal project to build an e-infrastructure for clinical metabolomics data (September 2015)
  5. Uppsala Universitet – PhenoMeNal: The gateway to individually adapted medication (September 2015)
  6. SciLifeLab – PhenoMeNal: a gateway to personalised medicine (September 2015)
  7. Life Science Sweden (Swedish Newspaper) – Tar fram infrastruktur för metabolomik (September 2015)
  8. Toxalim – The European project PhenoMeNal (Horizon 2020) launched on September 1st, 2015
  9. SNIC Science Cloud – Virtual Research Environments for Clinical Metabolomics (April 2016)
  10. Philippe Rocca-Sera. PhenoMeNal: Virtual e-Infrastructure supporting Clinical Research and Metabolism Studies. OeRC News. (November 2016)