Machine learning methods for biotechnology applications

We have developed a series of machine learning methods for biotechnology applications. These include screening of large metabolic databases and prediction of catalytic activity from sequence.

Further readings on catalytic activity prediction

  • Carbonell P, Wong J, Swainston N, Takano E, Turner NJ, Scrutton NS, Kell DB, Breitling R, Faulon JL. Selenzyme: Enzyme selection tool for pathway design. Bioinformatics, 2018. | doi: 10.1093/bioinformatics/bty065
  • Mellor J, Grigoras I, Carbonell P, Faulon JL. Semi-supervised Gaussian Process for automated enzyme search. ACS Synthetic Biology, 5(6): 518-528, 2016. | doi: 10.1021/acssynbio.5b00294
  • Carbonell P, Lecointre G, Faulon JL. Origins of specificity and promiscuity in metabolic networks. Journal of Biological Chemistry, 286(51): 43994-44004, 2011. | doi: 10.1074/jbc.M111.274050
  • Carbonell P, Faulon JL. Molecular signatures-based prediction of enzyme promiscuity. Bioinformatics, 26(16): 2012-2019, 2010. | doi: 10.1093/bioinformatics/btq317
  • Faulon JL, Misra M, Martin S, Sale K, Sapra R. Genome scale enzyme-metabolite and drug-target interaction predictions using the signature molecular descriptor. Bioinformatics. 2008 Jan 15;24(2):225-33. Epub 2007 Nov 23. | doi:  10.1093/bioinformatics/btm580

The same techniques are also used to predict molecular activity and to design novel molecules.

  • Koch M, Duigou T, Carbonell P, Faulon JL. Molecular structures enumeration and virtual screening in the chemical space with RetroPath2.0. Journal of Cheminformatics, 9:64, 2017. | doi: 10.1186/s13321-017-0252-9
  • Jaghoori, M.M., Jongmans S.T.Q., de Boer, F., Peironcely, J., Faulon, J.L., Reijmers, T., Hankemeier, T. PMG: Multi-core metabolite identification. Electronic Notes in Theoretical Computer Science, 299: 53-60, 2013. | doi: 10.1016/j.entcs.2013.11.005
  • Planson, A.G., Carbonell, P., Paillard, E., Pollet, N., Faulon, J.L. Compound toxicity screening and structure-activity relationship modeling in Escherichia coli. Biotechnology and Bioengineering, 109(3): 846-850, 2012. | doi: 10.1002/bit.24356