Dr Ke Yuan

Senior Lecturer (Therapeutic Science Research)

telephone: 01413306034
email: Ke.Yuan@glasgow.ac.uk

Biography

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/

Research interests

Research groups

Urological (Prostate/Bladder/Kidney)
Mesothelioma

Publications

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Jump to: 2025 | 2024 | 2023 | 2022 | 2021 | 2020 | 2018 | 2017 | 2016 | 2015 | 2014 | 2012 | 2011 | 2010 | 2009

Number of items: 37.

2025

Farndale, L., Insall, R. and Yuan, K. (2025) TriDeNT: Triple deep network training for privileged knowledge distillation in histopathology. Medical Image Analysis, (doi: 10.1016/j.media.2025.103479) (Early Online Publication)

Coudray, N. et al. (2025) Self supervised artificial intelligence predicts poor outcome from primary cutaneous squamous cell carcinoma at diagnosis. npj Digital Medicine, 8(1), 105. (doi: 10.1038/s41746-025-01496-3) (PMID:39955424) (PMCID:PMC11830021)

Lytras, S., Lamb, K. D., Ito, J., Grove, J. , Yuan, K. , Sato, K., Hughes, J. and Robertson, D. L. (2025) Pathogen genomic surveillance and the AI revolution. Journal of Virology, 99(2), e0160124. (doi: 10.1128/jvi.01601-24) (PMID:39878472) (PMCID:39878472)

Coudray, N., Occidental, M. A., Mantilla, J. G., Claudio Quiros, A., Yuan, K. , Balko, J., Tsirigos, A. and Jour, G. (2025) Quantitative and morphology-based deep convolutional neural network approaches for osteosarcoma survival prediction in the neoadjuvant and metastatic settings. Clinical Cancer Research, 31(2), pp. 365-375. (doi: 10.1158/1078-0432.ccr-24-2599) (PMID:39561274)

2024

Seyedshahi, F. et al. (2024) A histomorphological atlas of resected mesothelioma discovered by self-supervised learning from 3446 whole-slide images. bioRxiv, (doi: 10.1101/2024.11.18.624103)

Liu, D., Young, F., Lamb, K. D., Claudio Quiros, A., Pancheva, A., Miller, C. , Macdonald, C. , Robertson, D. L. and Yuan, K. (2024) PLM-interact: extending protein language models to predict protein-protein interactions. bioRxiv, (doi: 10.1101/2024.11.05.622169)

Liu, D., Young, F., Lamb, K. D., Robertson, D. L. and Yuan, K. (2024) Prediction of virus-host associations using protein language models and multiple instance learning. PLoS Computational Biology, 20(11), e1012597. (doi: 10.1371/journal.pcbi.1012597) (PMID:39561204) (PMCID:PMC11614202)

Lamb, K. D., Hughes, J. , Lytras, S., Young, F., Koci, O., Herzig, J., Lovell, S. C.,, Grove, J. , Yuan, K. and Robertson, D. L. (2024) From a single sequence to evolutionary trajectories: protein language models capture the evolutionary potential of SARS-CoV-2 protein sequences. bioRxiv, (doi: 10.1101/2024.07.05.602129)

Quiros, A. C. et al. (2024) Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides. Nature Communications, 15, 4596. (doi: 10.1038/s41467-024-48666-7) (PMID:38862472)

Liu, B. et al. (2024) Self-supervised learning reveals clinically relevant histomorphological patterns for therapeutic strategies in colon cancer. bioRxiv, (doi: 10.1101/2024.02.26.582106) (PMID:PPR813774)

Lamb, K. D., Luka, M. M., Saathoff, M., Orton, R. J. , Phan, M. V.T., Cotten, M. , Yuan, K. and Robertson, D. L. (2024) Mutational signature dynamics indicate SARS-CoV-2's evolutionary capacity is driven by host antiviral molecules. PLoS Computational Biology, 20(1), e1011795. (doi: 10.1371/journal.pcbi.1011795) (PMID:38271457) (PMCID:PMC10868779)

2023

Ji, Y., Cutiongco, M. F.A., Jensen, B. S. and Yuan, K. (2023) CP2Image: Generating High-Quality Single-Cell Images Using CellProfiler Representations. In: Medical Imaging with Deep Learning (MIDL 2023), Nashville, TN, USA, 10-12 July 2023, pp. 1-12.

2022

Ji, Y., Cutiongco, M. F.A., Jensen, B. S. and Yuan, K. (2022) CP2Image: Generating High-Quality Single-Cell Images Using CellProfiler Representations. NeurIPS 2022 Workshop on Learning Meaningful Representations of Life (LMRL 2022), 12 Sept 2022.

Liu, X., Yang, X., Ouyang, L., Guo, G., Su, J., Xi, R., Yuan, K. and Yuan, F. (2022) Protein Language Model Predicts Mutation Pathogenicity and Clinical Prognosis. NeurIPS 2022 Workshop on Learning Meaningful Representations of Life (LMRL 2022), 12 Sept 2022.

2021

Claudio Quiros, A., Coudray, N., Yeaton, A., Sunhem, W., Murray-Smith, R. , Tsirigos, A. and Yuan, K. (2021) Adversarial Learning of Cancer Tissue Representations. In: 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021), 27 Sept-1 Oct 2021, pp. 602-612. ISBN 9783030872366 (doi: 10.1007/978-3-030-87237-3_58)

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.

Bailey, M. H. et al. (2020) Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples. Nature Communications, 11, 4748. (doi: 10.1038/s41467-020-18151-y) (PMID:32958763) (PMCID:PMC7505971)

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.

Li, C. H. et al. (2020) Sex differences in oncogenic mutational processes. Nature Communications, 11, 4330. (doi: 10.1038/s41467-020-17359-2) (PMID:32859912) (PMCID:PMC7455744)

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. and Morris, Q. (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)

This list was generated on Fri Mar 28 14:00:59 2025 GMT.

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