Christopher Yau
University of Birmingham
Bayesian probabilityMarkov chain Monte CarloAlgorithmStatistical modelCancerInternal medicineSNPGenomeSingle-nucleotide polymorphismArtificial intelligenceGibbs samplingTranscriptomePattern recognitionGenomicsHidden Markov modelInferenceMathematicsComputer scienceProbabilistic logicCancer researchLatent variableGeneticsBioinformaticsComputational biologyMedicineBiology
Publications 84
#1Fabian Falck (University of Oxford)H-Index: 1
#1Fabian Falck (University of Oxford)H-Index: 2
Last. Christopher Holmes (University of Oxford)H-Index: 63
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Work in deep clustering focuses on finding a single partition of data. However, high-dimensional data, such as images, typically feature multiple interesting characteristics one could cluster over. For example, images of objects against a background could be clustered over the shape of the object and separately by the colour of the background. In this paper, we introduce Multi-Facet Clustering Variational Autoencoders (MFCVAE), a novel class of variational autoencoders with a hierarchy of latent...
1 Citations
#1Shivan SivakumarH-Index: 8
Last. Christopher YauH-Index: 28
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#1Mohammad KaramiNejadRanjbar (University of Oxford)H-Index: 11
#2Sahand Sharifzadeh (LMU: Ludwig Maximilian University of Munich)H-Index: 5
Last. Ahmed Ashour Ahmed (University of Oxford)H-Index: 28
view all 9 authors...
Bulk whole genome sequencing (WGS) enables the analysis of tumor evolution but, because of depth limitations, can only identify old mutational events. The discovery of current mutational processes for predicting the tumor's evolutionary trajectory requires dense sequencing of individual clones or single cells. Such studies, however, are inherently problematic because of the discovery of excessive false positive mutations when sequencing picogram quantities of DNA. Data pooling to increase the co...
4 CitationsSource
#1Mohammad KaramiNejadRanjbar (University of Oxford)H-Index: 11
#2Sahand Sharifzadeh (LMU: Ludwig Maximilian University of Munich)H-Index: 5
Last. Ahmed Ashour Ahmed (University of Oxford)H-Index: 28
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#1Zhiyuan Hu (University of Oxford)H-Index: 4
#2Mara Artibani (University of Oxford)H-Index: 7
Last. Ahmed Ashour Ahmed (University of Oxford)H-Index: 28
view all 27 authors...
Summary The inter-differentiation between cell states promotes cancer cell survival under stress and fosters non-genetic heterogeneity (NGH). NGH is, therefore, a surrogate of tumor resilience but its quantification is confounded by genetic heterogeneity. Here we show that NGH in serous ovarian cancer (SOC) can be accurately measured when informed by the molecular signatures of the normal fallopian tube epithelium (FTE) cells, the cells of origin of SOC. Surveying the transcriptomes of ∼6,000 FT...
34 CitationsSource
#1I de Santiago (University of Cambridge)H-Index: 3
#2Christopher Yau (University of Birmingham)H-Index: 28
Last. Shivan Sivakumar (University of Oxford)H-Index: 8
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: Pancreatic ductal adenocarcinoma (PDAC) is the most common malignancy of the pancreas and has one of the highest mortality rates of any cancer type with a 5-year survival rate of <5%. Recent studies of PDAC have provided several transcriptomic classifications based on separate analyses of individual patient cohorts. There is a need to provide a unified transcriptomic PDAC classification driven by therapeutically relevant biologic rationale to inform future treatment strategies. Here, we used a...
13 CitationsSource
#1Thanos P Mourikis (Francis Crick Institute)H-Index: 7
#2Lorena Benedetti (Francis Crick Institute)H-Index: 16
Last. Francesca D. Ciccarelli ('KCL': King's College London)H-Index: 30
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The identification of cancer-promoting genetic alterations is challenging particularly in highly unstable and heterogeneous cancers, such as esophageal adenocarcinoma (EAC). Here we describe a machine learning algorithm to identify cancer genes in individual patients considering all types of damaging alterations simultaneously. Analysing 261 EACs from the OCCAMS Consortium, we discover helper genes that, alongside well-known drivers, promote cancer. We confirm the robustness of our approach in 1...
18 CitationsSource
#1Zhiyuan HuH-Index: 4
Last. Ahmed Ashour AhmedH-Index: 28
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Accurate molecular classification in cancer is essential for rationalized therapy. However, achieving stable subtyping is highly challenging due to the underlying genomic complexity of tumors. An example of the most successful classification is in breast cancer where it is possible to link individual tumor types, basal vs luminal, to individual cell types of origin. However, the generalization of such an approach has not been easy because of the lack of knowledge about subtypes of the putative c...
We build upon probabilistic models for Boolean Matrix and Boolean Tensor factorisation that have recently been shown to solve these problems with unprecedented accuracy and to enable posterior inference to scale to Billions of observation. Here, we lift the restriction of a pre-specified number of latent dimensions by introducing an Indian Buffet Process prior over factor matrices. Not only does the full factor-conditional take a computationally convenient form due to the logical dependencies in...
May 24, 2019 in ICML (International Conference on Machine Learning)
#1Kaspar Märtens (University of Oxford)H-Index: 5
#2Kieran R. Campbell (UBC: University of British Columbia)H-Index: 13
Last. Christopher Yau (University of Birmingham)H-Index: 28
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The interpretation of complex high-dimensional data typically requires the use of dimensionality reduction techniques to extract explanatory low-dimensional representations. However, in many real-world problems these representations may not be sufficient to aid interpretation on their own, and it would be desirable to interpret the model in terms of the original features themselves. Our goal is to characterise how feature-level variation depends on latent low-dimensional representations, externa...
5 Citations