Dr Ke Yuan
- Lecturer in Machine Learning and Computational Biology (Computing Science)
- Affiliate (Institute of Cancer Sciences)
telephone:
01413306034
email:
Ke.Yuan@glasgow.ac.uk
School of Computing Science
https://orcid.org/0000-0002-2318-1460Biography
Ke Yuan is a Lecturer in Computing Science at the University of Glasgow. He received a PhD from the University of Southampton in 2013 advised by Mahesan Niranjan. Till 04/2016, He was a postdoctoral research fellow at Cancer Research UK Cambridge Institute at the University of Cambridge working with Florian Markowetz. He joined the School of Computing Science at the University of Glasgow in 05/2016.
Research group website: https://kyuanlab.org/
Publications
2021
Claudio Quiros, A., Murray-Smith, R. and Yuan, K. (2021) PathologyGAN: Learning deep representations of cancer tissue. Journal of Machine Learning for Biomedical Imaging, 2021(4), pp. 1-48.
Dentro, S. C. et al. (2021) Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes. Cell, 184(8), 2239-2254.e39. (doi: 10.1016/j.cell.2021.03.009) (PMID:33831375) (PMCID:PMC8054914)
Macintyre, G. et al. (2021) FrenchFISH: Poisson Models for Quantifying DNA Copy Number From Fluorescence In Situ Hybridization of Tissue Sections. JCO Clinical Cancer Informatics, 5, pp. 176-186. (doi: 10.1200/cci.20.00075) (PMID:33570999)
2020
Claudio Quiros, A., Murray-Smith, R. and Yuan, K. (2020) Learning a Low Dimensional Manifold of Real Cancer Tissue with PathologyGAN. NeurIPS 2020 Learning Meaningful Representations of Life, 11 Dec 2020.
Claudio Quiros, A., Murray-Smith, R. and Yuan, K. (2020) PathologyGAN: Learning Deep Representations of Cancer Tissue. In: Third Conference on Medical Imaging with Deep Learning, Montreal, Canada, 6-9 Jul 2020, pp. 669-695.
Gerstung, M. et al. (2020) The evolutionary history of 2,658 cancers. Nature, 578(7793), pp. 122-128. (doi: 10.1038/s41586-019-1907-7) (PMID:32025013)
The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium, et al. (2020) Pan-cancer analysis of whole genomes. Nature, 578(7793), pp. 82-93. (doi: 10.1038/s41586-020-1969-6) (PMID:32025007) (PMCID:PMC7025898)
Cmero, M. et al. (2020) Inferring structural variant cancer cell fraction. Nature Communications, 11, 730. (doi: 10.1038/s41467-020-14351-8) (PMID:32024845) (PMCID:PMC7002525)
Rubanova, Y., Shi, R., Harrigan, C. F., Li, R., Wintersinger, J., Sahin, N., Deshwar, A., Morris, Q. and , (2020) Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig. Nature Communications, 11, 731. (doi: 10.1038/s41467-020-14352-7) (PMID:32024834) (PMCID:PMC7002414)
2018
Tarabichi, M. et al. (2018) Neutral tumor evolution? Nature Genetics, 50(12), pp. 1630-1633. (doi: 10.1038/s41588-018-0258-x) (PMID:30374075) (PMCID:PMC6548558)
Dong, L.-Q. et al. (2018) Spatial and temporal clonal evolution of intrahepatic cholangiocarcinoma. Journal of Hepatology, 69(1), pp. 89-98. (doi: 10.1016/j.jhep.2018.02.029) (PMID:29551704)
2017
de Santiago, I., Liu, W., Yuan, K. , O'Reilly, M., Chilamakuri, C. S. R., Ponder, B. A.J., Meyer, K. B. and Markowetz, F. (2017) BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes. Genome Biology, 18, 39. (doi: 10.1186/s13059-017-1165-7) (PMID:28235418) (PMCID:PMC5326502)
2016
Marass, F., Mouliere, F., Yuan, K. , Rosenfeld, N. and Markowetz, F. (2016) A phylogenetic latent feature model for clonal deconvolution. Annals of Applied Statistics, 10(4), pp. 2377-2404. (doi: 10.1214/16-AOAS986)
2015
Yuan, K. , Sakoparnig, T., Markowetz, F. and Beerenwinkel, N. (2015) BitPhylogeny: a probabilistic framework for reconstructing intra-tumor phylogenies. Genome Biology, 16(1), p. 36. (doi: 10.1186/s13059-015-0592-6) (PMID:25786108) (PMCID:PMC4359483)
Wang, X., Yuan, K. and Markowetz, F. (2015) Joining the dots: network analysis of gene perturbation data. In: Markowetz, F. and Boutros, M. (eds.) Systems Genetics: Linking Genotypes and Phenotypes. Series: Cambridge series in systems genetics. Cambridge University Press. ISBN 9781107013841
2014
Wang, X., Yuan, K. , Hellmayr, C., Liu, W. and Markowetz, F. (2014) Reconstructing evolving signalling networks by hidden Markov nested effects models. Annals of Applied Statistics, 8(1), pp. 448-480. (doi: 10.1214/13-AOAS696)
2012
Yuan, K. , Girolami, M. and Niranjan, M. (2012) Markov chain Monte Carlo methods for state-space models with point process observations. Neural Computation, 24(6), pp. 1462-1486. (doi: 10.1162/NECO_a_00281) (PMID:22364499)
2011
Mangion, A. Z., Yuan, K. , Kadirkamanathan, V., Niranjan, M. and Sanguinetti, G. (2011) Online variational inference for state-space models with point-process observations. Neural Computation, 23(8), pp. 1967-1999. (doi: 10.1162/NECO_a_00156)
2010
Yuan, K. and Niranjan, M. (2010) Estimating a state-space model from point process observations: a note on convergence. Neural Computation, 22(8), pp. 1993-2001. (doi: 10.1162/neco.2010.07-09-1047) (PMID:20337540)
2009
Yuan, K. , Liu, W. and Yang, L.-L. (2009) Reliability-Aided Multiuser Detection in Time-Frequency-Domain Spread Multicarrier DS-CDMA Systems. In: IEEE 69th Vehicular Technology Conference, 26-29 April 2009, pp. 1-5. (doi:10.1109/VETECS.2009.5073833)
Articles
Claudio Quiros, A., Murray-Smith, R. and Yuan, K. (2021) PathologyGAN: Learning deep representations of cancer tissue. Journal of Machine Learning for Biomedical Imaging, 2021(4), pp. 1-48.
Dentro, S. C. et al. (2021) Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes. Cell, 184(8), 2239-2254.e39. (doi: 10.1016/j.cell.2021.03.009) (PMID:33831375) (PMCID:PMC8054914)
Macintyre, G. et al. (2021) FrenchFISH: Poisson Models for Quantifying DNA Copy Number From Fluorescence In Situ Hybridization of Tissue Sections. JCO Clinical Cancer Informatics, 5, pp. 176-186. (doi: 10.1200/cci.20.00075) (PMID:33570999)
Gerstung, M. et al. (2020) The evolutionary history of 2,658 cancers. Nature, 578(7793), pp. 122-128. (doi: 10.1038/s41586-019-1907-7) (PMID:32025013)
The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium, et al. (2020) Pan-cancer analysis of whole genomes. Nature, 578(7793), pp. 82-93. (doi: 10.1038/s41586-020-1969-6) (PMID:32025007) (PMCID:PMC7025898)
Cmero, M. et al. (2020) Inferring structural variant cancer cell fraction. Nature Communications, 11, 730. (doi: 10.1038/s41467-020-14351-8) (PMID:32024845) (PMCID:PMC7002525)
Rubanova, Y., Shi, R., Harrigan, C. F., Li, R., Wintersinger, J., Sahin, N., Deshwar, A., Morris, Q. and , (2020) Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig. Nature Communications, 11, 731. (doi: 10.1038/s41467-020-14352-7) (PMID:32024834) (PMCID:PMC7002414)
Tarabichi, M. et al. (2018) Neutral tumor evolution? Nature Genetics, 50(12), pp. 1630-1633. (doi: 10.1038/s41588-018-0258-x) (PMID:30374075) (PMCID:PMC6548558)
Dong, L.-Q. et al. (2018) Spatial and temporal clonal evolution of intrahepatic cholangiocarcinoma. Journal of Hepatology, 69(1), pp. 89-98. (doi: 10.1016/j.jhep.2018.02.029) (PMID:29551704)
de Santiago, I., Liu, W., Yuan, K. , O'Reilly, M., Chilamakuri, C. S. R., Ponder, B. A.J., Meyer, K. B. and Markowetz, F. (2017) BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes. Genome Biology, 18, 39. (doi: 10.1186/s13059-017-1165-7) (PMID:28235418) (PMCID:PMC5326502)
Marass, F., Mouliere, F., Yuan, K. , Rosenfeld, N. and Markowetz, F. (2016) A phylogenetic latent feature model for clonal deconvolution. Annals of Applied Statistics, 10(4), pp. 2377-2404. (doi: 10.1214/16-AOAS986)
Yuan, K. , Sakoparnig, T., Markowetz, F. and Beerenwinkel, N. (2015) BitPhylogeny: a probabilistic framework for reconstructing intra-tumor phylogenies. Genome Biology, 16(1), p. 36. (doi: 10.1186/s13059-015-0592-6) (PMID:25786108) (PMCID:PMC4359483)
Wang, X., Yuan, K. , Hellmayr, C., Liu, W. and Markowetz, F. (2014) Reconstructing evolving signalling networks by hidden Markov nested effects models. Annals of Applied Statistics, 8(1), pp. 448-480. (doi: 10.1214/13-AOAS696)
Yuan, K. , Girolami, M. and Niranjan, M. (2012) Markov chain Monte Carlo methods for state-space models with point process observations. Neural Computation, 24(6), pp. 1462-1486. (doi: 10.1162/NECO_a_00281) (PMID:22364499)
Mangion, A. Z., Yuan, K. , Kadirkamanathan, V., Niranjan, M. and Sanguinetti, G. (2011) Online variational inference for state-space models with point-process observations. Neural Computation, 23(8), pp. 1967-1999. (doi: 10.1162/NECO_a_00156)
Yuan, K. and Niranjan, M. (2010) Estimating a state-space model from point process observations: a note on convergence. Neural Computation, 22(8), pp. 1993-2001. (doi: 10.1162/neco.2010.07-09-1047) (PMID:20337540)
Book Sections
Wang, X., Yuan, K. and Markowetz, F. (2015) Joining the dots: network analysis of gene perturbation data. In: Markowetz, F. and Boutros, M. (eds.) Systems Genetics: Linking Genotypes and Phenotypes. Series: Cambridge series in systems genetics. Cambridge University Press. ISBN 9781107013841
Conference or Workshop Item
Claudio Quiros, A., Murray-Smith, R. and Yuan, K. (2020) Learning a Low Dimensional Manifold of Real Cancer Tissue with PathologyGAN. NeurIPS 2020 Learning Meaningful Representations of Life, 11 Dec 2020.
Conference Proceedings
Claudio Quiros, A., Murray-Smith, R. and Yuan, K. (2020) PathologyGAN: Learning Deep Representations of Cancer Tissue. In: Third Conference on Medical Imaging with Deep Learning, Montreal, Canada, 6-9 Jul 2020, pp. 669-695.
Yuan, K. , Liu, W. and Yang, L.-L. (2009) Reliability-Aided Multiuser Detection in Time-Frequency-Domain Spread Multicarrier DS-CDMA Systems. In: IEEE 69th Vehicular Technology Conference, 26-29 April 2009, pp. 1-5. (doi:10.1109/VETECS.2009.5073833)
