2024

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., 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)