Dr Nicos Angelopoulos
Lecturer in Computational Systems Immunity
Overview
I am a computer scientist working on computational and statistical aspects of
bio data analytics. I use knowledge representation methods and tools in machine
learning as well as in building practical, well engineered scientific code.
Currently a lecturer in Computational Systems Immunity at
Cardiff University's Systems Immunity Research Institute, working in close
collaboration with the sepsis group.
Previously I worked on (a) methodological projects in Markov chain Monte Carlo simulations
for Bayesian machine learning over priors defined with probabilistic logic programs
(Bims, University of York), (b) mass spectormetry functional data analytics (Imperial College)
and (c) genomic models of cancer evolution and cancer precision medicine (Sanger Institute).
I am staunch proponent of open source software, both in the systems I use in my
research (SWI-Prolog, Linux, R, Latex) and in publishing open source code with all my
papers (github page).
Biography
Since my PhD I worked as a researcher on a number of projects in the UK and the Netherlands.
The last few years my research has focused on the development and application of principled
formalisms that combine logic and probability along with associated stochastic algorithms in computational biology.
Previously I worked in a number of research positions, including
- senior staff scientist in applied statistics at the Sanger Institute's Cancer Genome Project (now CASM)
- research fellow at University of York where I worked with James Cussens
Academic positions
July 2020 Joined Medical School at Cardiff University
2019-20 Lecturer in AI and Computational Biology - Essex University
School of Computer Science and Electronic Engineering
2015-18 Senior Staff Scientist in Applied Statistics - Sanger Institute
Cancer and Somatic Mutations Section
2014-15 Researcher in proteomics data analytics - Imperial College
Cancer Signalling group
2010 - 13 Researcher in Computational Cancer Biology - Netherlands Cancer Institute
Compuational biology group
2009 Senior Scientific officer - Institute of Cancer Research, London
Computational cancer biology group.
2006-8 Research in Computational statistics - Edinburgh University
Systems bio-physics group.
2003-5 Research in Bayesian machine learnin - University of York
James Cussens' group.
Committees and reviewing
Senior PC member IJCAI 2021 (International Joint Conference on AI)
Workshop chair ICLP 2001 (International Conference of Logic Programming)
PC member
- IJCAI 2021 (Senior)
- IJCAI-PRICAI 2020
- ICLP 2020
- CMSB 2020 (Comp. Meth in Sys Bio)
- CBMS 2020 (33rd Int. Symp. on Comp. Based Med. Sys)
- CP 2017 (Bioinformatics Track)
- IJCAI 2015
- Ciclops 2013ProBioMed 2011 (Probabilistic problem solving in biomedicine),
- MLG 2008 + 2009 (Machine Learning with Graphs).
Review Editor Computational Intelligence section of Frontiers in Robotics & AI, (2014-2020)
Reviewer
- Expert Systems With Applications 2020
- Machine Learning 2019 (ILP special issue)
- BMC Genomics 2019
- AI Reviews, 2017
- Theory and Practice of Logic Programming, 2018
- Project reviewer for FWO (Belgian research council), 2016
- J. of Molecular Modelling, 2010-2020
- Bioinformatics, 2012, 2017-8
- Machine Learning J., 2009
- ECML, 2004
Workshop series initiator: PLP Series, 2014-20, workshop on Probabilistic Logic Programming
Publications
2022
- Angelopoulos, N., Chatzipli, A., Nangalia, J., Maura, F. and Campbell, P. J. 2022. Bayesian networks elucidate complex genomic landscapes in cancer. Communications Biology 5(1), article number: 306. (10.1038/s42003-022-03243-w)
2021
- Rustad, E. H. et al. 2021. mmsig: a fitting approach to accurately identify somatic mutational signatures in hematological malignancies. Communications Biology 4(1), article number: 424. (10.1038/s42003-021-01938-0)
2020
- Rustad, E. H. et al. 2020. Revealing the impact of structural variants in multiple myeloma. Blood Cancer Discovery 1(3), pp. 258-273. (10.1158/2643-3230.BCD-20-0132)
- Rustad, E. H. et al. 2020. Timing the initiation of multiple myeloma. Nature Communications 11(1), article number: 1917. (10.1038/s41467-020-15740-9)
- Jones, S. et al. 2020. Targeting of EGFR by a combination of antibodies mediates unconventional EGFR trafficking and degradation. Scientific Reports 10(1), article number: 663. (10.1038/s41598-019-57153-9)
- Draaisma, K. et al. 2020. Molecular evolution of IDH wild-type glioblastomas treated with standard of care affects survival and design of precision medicine trials: a report from the EORTC 1542 study. Journal of Clinical Oncology 38(1), pp. 81-99. (10.1200/JCO.19.00367)
2019
- Maura, F. et al. 2019. Timing the initiation of multiple myeloma. Presented at: 61st American Society of Hematology Annual Meeting, Orlando, FL, USA, 7-10 December 2019, Vol. 134. Vol. Supple. American Society of Hematology, (10.1182/blood-2019-124357)
- Maura, F. et al. 2019. Timing the initiation of multiple myeloma. Presented at: 17th International Myeloma Workshop, Boston, MA, USA, 12-15 September 2019, Vol. 19. Vol. 10, Su. Elsevier pp. E6-E7., (10.1016/j.clml.2019.09.008)
- Angelopoulos, N. and Wielemaker, J. 2019. Advances in big data bio analytics. Presented at: 35th International Conference on Logic Programming (CLP 2019), Las Cruces, NM, USA, 20-25 September 2019Proceedings 35th International Conference on Logic Programming (Technical Communications), Vol. 306. ETCS pp. 309–322.
- Maura, F. et al. 2019. Genomic landscape and chronological reconstruction of driver events in multiple myeloma. Nature Communications 10, article number: 3835. (10.1038/s41467-019-11680-1)
2018
- Maura, F. et al. 2018. The landscape of structural variant sgnatures in multiple myeloma identifies distinct disease subgroups with implications for pathogenesis. Presented at: 60th American Society of Hematology Annual Meeting, San Diego, CA, USA, 1-4 December 2018, Vol. 132. Vol. Supple. American Society of Hematology, (10.1182/blood-2018-99-112420)
- Maura, F. et al. 2018. The genomic landscape of structural variations and complex events in multiple myeloma. Presented at: XV Congress of the Italian Society of Experimental Hematology, Rimini, Italy, 18-20 October 2018, Vol. 103. Vol. S3. pp. S14.
- Bolli, N. et al. 2018. Analysis of mutations and structural variants to redefine the genomic landscape of multiple myeloma and its clinical implications. Presented at: XV Congress of the Italian Society of Experimental Hematology, Rimini, Italy, 18-20 October 2018, Vol. 103. Vol. S3. Ferrata Storti Foundation pp. S51.
- Grinfeld, J. et al. 2018. Classification and personalized prognosis in myeloproliferative neoplasms. New England Journal of Medicine 379(15), pp. 1416-1430. (10.1056/NEJMoa1716614)
- Mitchell, T. J. et al. 2018. Timing the landmark events in the evolution of clear cell renal cell cancer: TRACERx Renal. Cell 173(3), pp. 611-623.e17. (10.1016/j.cell.2018.02.020)
- Maura, F. et al. 2018. Biological and prognostic impact of APOBEC-induced mutations in the spectrum of plasma cell dyscrasias and multiple myeloma cell lines. Leukemia 32(4), pp. 1043-1047. (10.1038/leu.2017.345)
2017
- Maura, F. et al. 2017. The genomic landscape of multiple myeloma complex structural variations. Presented at: 59th American Society of Hematology Annual Meeting, Atlanta, GA, USA, 9-12 December 2-17, Vol. 130. Vol. Supple. American Society of Hematology, (10.1182/blood.V130.Suppl_1.330.330)
- Maura, F. et al. 2017. Biological and prognostic impact of apobec-induced mutations in the spectrum of plasma cell dyscrasias. Blood 130(Supple), article number: 1771. (10.1182/blood.V130.Suppl_1.1771.1771)
- Angelopoulos, N. and Wielemaker, J. 2017. Accessing biological data as Prolog facts. Presented at: 19th International Symposium on Principles and Practice of Declarative Programming, Namur, Belgium, 9-12 October 2017PPDP '17: Proceedings of the 19th International Symposium on Principles and Practice of Declarative Programming. ACM pp. 29-38., (10.1145/3131851.3131857)
- Grinfeld, J. et al. 2017. Personalized prognostic predictions for patients with myeloproliferative neoplasms through integration of comprehensive genomic and clinical information. Blood 130(Supple), pp. 491. (10.1182/blood.V130.Suppl_1.491.491)
- Angelopoulos, N. and Cussens, J. 2017. Distributional logic programming for Bayesian knowledge representation. International Journal of Approximate Reasoning 80, pp. 52-66. (10.1016/j.ijar.2016.08.004)
- Maura, F. et al. 2017. The apobec mutational activity in multiple myeloma: from diagnosis to cell lines. Presented at: 46th Congress of the Italian Society of Hematology, Rome, Italy, 15-18 October 2017, Vol. 102. Vol. S3. Ferrata Storti Foundation pp. 124.
2016
- Angelopoulos, N., Abdallah, S. and Giamas, G. 2016. Advances in integrative statistics for logic programming. International Journal of Approximate Reasoning 78, pp. 103-115. (10.1016/j.ijar.2016.06.008)
- Favicchio, R. et al. 2016. Choline metabolism is an early predictor of EGFR-mediated survival in NSCLC. Presented at: 107th Annual Meeting of the American Association for Cancer Research, New Orleans, LA, USA, 16-20 April 2016Proceedings of the 107th Annual Meeting of the American Association for Cancer Research, Vol. 76. Vol. 14. Philadelphia, PA, USA: American Association for Cancer Research pp. 4235., (10.1158/1538-7445.AM2016-4235)
- Angelopoulos, N., Stebbing, J., Xu, Y., Giamas, G. and Zhang, H. 2016. Proteome-wide dataset supporting functional study of tyrosine kinases in breast cancer. Data in Brief 7, pp. 740-746. (10.1016/j.dib.2016.03.024)
- Nunes, J. et al. 2016. ATG9A loss confers resistance to trastuzumab via c-Cbl mediated Her2 degradation. Oncotarget 7(19), pp. 27599-27612. (10.18632/oncotarget.8504)
2015
- Stebbing, J., Zhang, H., Xu, Y., Grothey, A., Ajuh, P., Angelopoulos, N. and Giamas, G. 2015. Characterization of the Tyrosine Kinase-Regulated Proteome in Breast Cancer by Combined use of RNA interference (RNAi) and Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) Quantitative Proteomics. Molecular and Cellular Proteomics 151, pp. 555–568. (10.1074/mcp.M115.048090)
- Xu, Y. et al. 2015. LMTK3 represses tumor suppressor-like genes through chromatin remodeling in breast cancer. Cell Reports 12(5), pp. 837-849. (10.1016/j.celrep.2015.06.073)
- Zhang, H., Angelopoulos, N., Xu, Y., Grothey, A., Nunes, J., Stebbing, J. and Giamas, G. 2015. Proteomic profile of KSR1-regulated signalling in response to genotoxic agents in breast cancer. Breast Cancer Research and Treatment 151(3), pp. 555-568. (10.1007/s10549-015-3443-y)
- Angelopoulos, N. and Giamas, G. 2015. A logical approach to working with biological databases. Presented at: 31st International Conference on Logic Programming (ICLP 2015), Cork, Ireland, 31 August - 4 September 2015Proceedings of the Technical Communications of the 31st International Conference on Logic Programming (ICLP 2015), Vol. 1433. CEUR
- MacIntyre, D. A. et al. 2015. The vaginal microbiome during pregnancy and the postpartum period in a European population. Scientific Reports 5, article number: 8988. (10.1038/srep08988)
- Spanjaard, E. et al. 2015. Quantitative imaging of focal adhesion dynamics and their regulation by HGF and Rap1 signaling. Experimental Cell Research 330(2), pp. 382-397. (10.1016/j.yexcr.2014.10.012)
2010
- Husi, H. et al. 2010. Selective chemical intervention in the proteome of Caenorhabditis elegans. Journal of Proteome Research 9(11), pp. 6060–6070. (10.1021/pr100427c)
2009
- Angelopoulos, N., Hadjiprocopis, A. and Walkinshaw, M. D. 2009. Bayesian model averaging for ligand discovery. Journal of Chemical Information and Modeling 49(6), pp. 1547-1557. (10.1021/ci900046u)
- Angelopoulos, N. and Cussens, J. 2009. Bayesian learning of Bayesian networks with informative priors. Annals of Mathematics and Artificial Intelligence 54, pp. 53-98. (10.1007/s10472-009-9133-x)
2001
- Angelopoulos, N. and Cussens, J. 2001. Markov chain Monte Carlo using tree-based priors on model structure.. Presented at: 17th The Conference on Uncertainty in Artificial Intelligence (UAI 2001), Seattle, WA, USA, 2-5 August 2001 Presented at Breese, J. and Koller, D. eds.UAI'01: Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence. San Francisco, USA: Morgan Kaufmann Publishers Inc., (10.5555/2074022.2074025)
I have conducted my research at prestigious UK and Dutch universities and institutes.
My area of expertise lies in the intersection of machine learning,
artificial intelligence and computational biology.
I am interested in theories that can model uncertainty and
have strong foundations in probability theory along with computational systems
that can reason with and learn such complex models from large data sets.
- during 2000-2010 I worked closely with James Cussens in York
in developing an elegant Probabilistic Logic Programming language
for Bayesian machine learning Bims. We published a number of papers
on theoretical aspects of the approach in high profile computer science conferences
such as UAI and IJCAI as well as on the application to specific datasets. - since 2011 I have been working in computational biology using AI techniques.
I have pursued open source and open data research throughout my career, having published open source
analysis methods and tools with the vast majority of my papers.
My work in computational biology includes theoretical work blending knowledge and inference with logical
and probabilistic foundations. My work also encompasses applied tools such as Real, which provides a
powerful bridge to the R statistical programming environment. - while at Sanger, I worked on high quality clinical and mutational datasets including survival analysis, precision oncology and cancer evolution based on mutational patterns in diverse cancers including hematological and solid tumour cancers. I was fortunate to work on a number papers, and substantially contributed to 3 substantial papers:
- GrinfeldJ+2018 (NEJM): precision oncology in myeloproliferative neoplasm
- MitchellTJ+2018 (Cell): statistical inference of landmark events in clear cell renal cell cancer
- MauraF+2019 (Nat Coms): Bayesian networks for cancer epistasis in multiple myeloma