Publications (Computational Biology and Bioinformatics) | Department of Statistics - University of Oxford

Publications by Computational Biology and Bioinformatics

Madsen, C., Hein, J. and Workman, C. (2022) “Systematic inference of indirect transcriptional regulation by protein kinases and phosphatases”, PLoS Computational Biology, 18(6), pp. e1009414 - e1009414.

Pardo-Diaz, J. et al. (2022) “Generating weighted and thresholded gene coexpression networks using signed distance correlation.”, Network Science, 10(2), pp. 131–145.

Bentley, M. et al. (2022) “Pleiotropic constraints promote the evolution of cooperation in cellular groups”, PLoS Biology, 20(6).

Chadeau-Hyam, M. et al. (2022) “Breakthrough SARS-CoV-2 infections in double and triple vaccinated adults and single dose vaccine effectiveness among children in Autumn 2021 in England: REACT-1 study”, EClinicalMedicine, 48, p. 101419.

Parag, K., Thompson, R. and Donnelly, C. (2022) “Are epidemic growth rates more informative than reproduction numbers?”, Journal of the Royal Statistical Society: Series A, 185(S1), pp. S5 - S15.

Pardo-Diaz, J. et al. (2022) “Extracting information from gene coexpression networks of Rhizobium leguminosarum”, Journal of Computational Biology, 29(7), pp. 752–768.

Penn, M. and Donnelly, C. (2022) “Analysis of a double Poisson model for predicting football results in Euro 2020”, PLoS One, 17(5).

Mahajan, A. et al. (2022) “Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation”, Nature Genetics, 54(5), pp. 560–572.

He, Y. et al. (2022) “SSSNET: semi-supervised signed network clustering”, in Proceedings of the SIAM International Conference on Data Mining (SDM22). Society for Industrial and Applied Mathematics, pp. 244–252.

Parag, K. and Donnelly, C. (2022) “Fundamental limits on inferring epidemic resurgence in real time using effective reproduction numbers”, PLoS Computational Biology, 18(4).