Dr Justina Zurauskiene PhD

Dr Justina Zurauskiene

Institute of Cancer and Genomic Sciences
BRIDGE Fellow - Population Dynamics and Women’s Health

Contact details

Address
Centre for Computational Biology
Institute of Cancer and Genomic Sciences
Haworth Building
University of Birmingham
Edgbaston
Birmingham B15 2TT

Dr Justina Žurauskienė is a BRIDGE Fellow at the Institute of Cancer and Genomic Sciences and currently is based at the Centre for Computational Biology. She has expertise and research interest in mathematical modelling, statistical machine learning and method development with applications focusing on genomic data and women’s health.

Qualifications

  • PhD (Computational Biology), Imperial College London
  • Masters degree (Mathematics), Vilnius University
  • Bachelor degree (Mathematics and Applications of Mathematics), Vilnius University

Biography

Dr Justina Žurauskienė obtained formal training in mathematics at Vilnius University (2010).

During her master’s studies she completed four months Erasmus Internship program at The Theoretical Systems Biology Group, Imperial College London, where she remained to pursue a doctorate in computational biology (2014).

Subsequently she undertook a three years postdoctoral research position in a single cell gemonics at The Genomic Medicine Group, The Wellcome Trust Centre for Human Genetics, University of Oxford (2017).

In 2017 she joined University of Birmingham as a BRIDGE fellow focusing on population dynamics and women’s health research.

Teaching

Bioinformatics MSc/Diploma/Certificate (statistical machine learning)

Research

Dr Žurauskienė’s research is broadly focused on development of new statistical and computational tools to address challenges posed by large-scale and high-dimensional data (e.g. genomic data). Modelling dynamical systems that involve complex interactions between several components such as genetics, environment and life style; also teasing apart scientifically interesting patterns and trends from natural biological heterogeneity and underlying confounding factors. Methodological applications are focusing on women’s health research, in particular maternal health studies and child development.

In the past Dr Žurauskienė’s research was concerned with modelling various processes at the single cell level, developing tools for single cell data analysis using Bayesian non-parametric methods, developing clustering algorithms for data integration and hierarchical modelling.

Publications

  • Žurauskienė, J., Yau, C. (2016), pcaRreduce: Hierarchical Clustering of Single Cell Transcriptional Profiles, BMC Bioinformatics, 17:140, DOI 10.1186/s12859-016-0984-y
  • Žurauskienė*, J., Kirk*, P.D.W., Stumpf, M.P.H. (2016), A graph theoretical approach to data fusion, Stat. Appl. Genet. Mol. Biol., 15,2:107-122, DOI 10.1515/sagmb-2016-0016
  • Žurauskienė, J., Kirk, P., Thorne, T., Stumpf, M.P.H. (2014), Bayesian non-parametric approaches to reconstructing oscillatory systems and the Nyquist limit, Physica A: Statistical Mechanics and its Applications, 407:33-42.
  • Žurauskienė, J., Kirk, P., Thorne, T., Pinney, J., Stumpf, M.P.H. (2014), Derivative processes for modelling metabolic fluxes, Bioinformatics, 30,13:1892-1898.
  • Komorowski, M., Žurauskienė, J., Stumpf, M.P.H. (2012), StochSens—matlab package for sensitivity analysis of stochastic chemical systems, Bioinformatics, 28,5:731-733.
* should be joint first authors

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