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Machine Learning and Data Analytics for Protein Science

About

Molecular dynamics simulations of proteins have had a great impact on the understanding of biological function and pathogenic effects of mutations. The opportunity for faster simulation algorithms and GPU-parallelisation has boosted the ability to investigate proteins moving from single cases to large studies. Despite this, a great challenge remains in extracting meaningful data from larger terabyte-size trajectories. We have developed several methods to address these challenges using data analytics and machine learning.

Research papers

  • Teletin, M., Czibula, G., Bocicor, M-I., Albert, S., Pandini, A. (2018) ''. Lecture Notes in Computer Science, 11140 pp. 79 - 89. ISSN: 0302-9743
  • Tiberti, M., Pandini, A., Fraternali, F., Fornili, A. (2017) ''. Bioinformatics, Volume 34, Issue 2, pp. 207–214. ISSN: 1367-4803
  • Chung, SS., Pandini, A., Annibale, A., Coolen, ACC., Thomas, NSB., Fraternali, F. (2015) ''. Scientific Reports volume 5, pp. 8540. ISSN: 2045-2322
  • Fornili, A., Pandini, A., Lu, H-C., Fraternali, F. (2013) ''. Journal of Chemical Theory and Computation, 9:11, pp. 5127 - 5147. ISSN: 1549-9618
  • Pandini, A., Fornili, A., Fraternali, F., Kleinjung, J. (2012) ''.  FASEB Journal, 26:2, pp. 868 - 881. ISSN: 0892-6638
  • Fraccalvieri, D., Pandini, A., Stella, F., Bonati, L (2011) ''. BMC Bioinformatics, 12:158, 2011. ISSN: 1471-2105
  • Pandini, A., Fornili, A., Kleinjung, J. (2010) ''. BMC Bioinformatics, 11. ISSN: 1471-2105