An ambitious EC-funded research initiative on epigenetics advancing towards systems biology 65

Computer Laboratory, University of Cambridge, Cambridge, United Kingdom

Statistical Bioinformatics, Systems Biology, Mathematical modeling

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I am interested in statistical bioinformatics and mathematical modeling methodologies. Statistical bioinformatics addresses multi omics integration with focus on epigenetics, HIC and gene expression data. This approach leads to parameter estimation for mathematical modeling and to the development of software tools useful for the biologists community. The mathematical modeling focuses on complex diseases and comorbidities such as inflammation, diabetes, bone diseases and cancer which have an important epigenetic component. The integration of statistical bioinformatics and mathematical modeling generates predictive models in personalised medicine, methods for combining Multi scale, multi omics and multi physics modelling of molecules-cell-tissue-organ interactions.

People involved:

  • Hui Xiao (1st year PhD)
  • Yoli Shavit (3rd year PhD)
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Latest publications

Erratum: Predictive analytics of environmental adaptability in multi-omic network models.

27199183 – 2016-05-21
Sci Rep 2016 May 20;6:26266
Angione C, Lió P

Muxstep: an open-source C ++ multiplex HMM library for making inferences on multiple data types.

27153633 – 2016-05-07
Bioinformatics 2016 Apr 13;
Veličković P, Liò P

Protein Interaction Networks Link Schizophrenia Risk Loci to Synaptic Function.

27056717 – 2016-04-09
Schizophr Bull 2016 Apr 7;
Schwarz E, Izmailov R, Liò P, Meyer-Lindenberg AView all their publications

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