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

Publications by Computational Biology and Bioinformatics

Lu, Y., Reinert, G. and Cucuringu, M. (2023) “Co-trading networks for modeling dynamic interdependency structures and estimating high-dimensional covariances in US equity markets”, arXiv.

Lu, Y., Reinert, G. and Cucuringu, M. (2023) “Co-Trading Networks for Modeling Dynamic Interdependency Structures and Estimating High-Dimensional Covariances in US Equity Markets”, SSRN Electronic Journal.

Eales, O. et al. (2023) “The use of representative community samples to assess SARS-CoV-2 lineage competition: Alpha outcompetes Beta and wild-type in England from January to March 2021”, Microbial Genomics, 9(2).

Penn, M. and Donnelly, C. (2023) “Asymptotic analysis of optimal vaccination policies”, Bulletin of Mathematical Biology, 85.

Agarwal, I. et al. (2023) “Relating pathogenic loss-of-function mutations in humans to their evolutionary fitness costs”, eLife, 12, p. e83172.

Raybould, M. et al. (2023) “Computationally profiling peptide: MHC recognition by T-cell receptors and T-cell receptor-mimetic antibodies”, Frontiers in Immunology, 13.

McCabe, R. et al. (2023) “Alternative epidemic indicators for COVID-19: a model-based assessment of COVID-19 mortality ascertainment in three settings with incomplete death registration systems”, medRxiv.

Spoendlin, F. et al. (2023) “Improved computational epitope profiling using structural models identifies a broader diversity of antibodies that bind the same epitope”, bioRxiv.

Watson, L. et al. (2023) “Improving estimates of epidemiological quantities by combining reported cases with wastewater data: a statistical framework with applications to COVID-19 in Aotearoa New Zealand”, medRxiv.

Atchison, C. et al. (2023) “LONG-TERM PHYSICAL AND MENTAL HEALTH IMPACT OF COVID-19 ON ADULTS IN ENGLAND: FOLLOW UP OF A LARGE RANDOM COMMUNITY SAMPLE”, medRxiv.