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  • CONFERENCE PAPER
    Altuncu MT, Mayer E, Yaliraki SN, Barahona Met al., 2018,

    From Text to Topics in Healthcare Records: An Unsupervised Graph Partitioning Methodology

    Electronic Healthcare Records contain large volumes of unstructured data,including extensive free text. Yet this source of detailed information oftenremains under-used because of a lack of methodologies to extract interpretablecontent in a timely manner. Here we apply network-theoretical tools to analysefree text in Hospital Patient Incident reports from the National HealthService, to find clusters of documents with similar content in an unsupervisedmanner at different levels of resolution. We combine deep neural networkparagraph vector text-embedding with multiscale Markov Stability communitydetection applied to a sparsified similarity graph of document vectors, andshowcase the approach on incident reports from Imperial College Healthcare NHSTrust, London. The multiscale community structure reveals different levels ofmeaning in the topics of the dataset, as shown by descriptive terms extractedfrom the clusters of records. We also compare a posteriori against hand-codedcategories assigned by healthcare personnel, and show that our approachoutperforms LDA-based models. Our content clusters exhibit good correspondencewith two levels of hand-coded categories, yet they also provide further medicaldetail in certain areas and reveal complementary descriptors of incidentsbeyond the external classification taxonomy.

  • CONFERENCE PAPER
    Altuncu MT, Yaliraki SN, Barahona M, 2018,

    Content-driven, unsupervised clustering of news articles through multiscale graph partitioning

    The explosion in the amount of news and journalistic content being generatedacross the globe, coupled with extended and instantaneous access to informationthrough online media, makes it difficult and time-consuming to monitor newsdevelopments and opinion formation in real time. There is an increasing needfor tools that can pre-process, analyse and classify raw text to extractinterpretable content; specifically, identifying topics and content-drivengroupings of articles. We present here such a methodology that brings togetherpowerful vector embeddings from Natural Language Processing with tools fromGraph Theory that exploit diffusive dynamics on graphs to reveal naturalpartitions across scales. Our framework uses a recent deep neural network textanalysis methodology (Doc2vec) to represent text in vector form and thenapplies a multi-scale community detection method (Markov Stability) topartition a similarity graph of document vectors. The method allows us toobtain clusters of documents with similar content, at different levels ofresolution, in an unsupervised manner. We showcase our approach with theanalysis of a corpus of 9,000 news articles published by Vox Media over oneyear. Our results show consistent groupings of documents according to contentwithout a priori assumptions about the number or type of clusters to be found.The multilevel clustering reveals a quasi-hierarchy of topics and subtopicswith increased intelligibility and improved topic coherence as compared toexternal taxonomy services and standard topic detection methods.

  • JOURNAL ARTICLE
    Arnaudon A, 2018,

    Structure preserving noise and dissipation in the Toda lattice

    , Journal of Physics A: Mathematical and Theoretical, Vol: 51, ISSN: 1751-8113

    In this paper, we use Flaschka's change of variables of the open Toda latticeand its interpretation in term of the group structure of the LU factorisationas a coadjoint motion on a certain dual of Lie algebra to implement a structurepreserving noise and dissipation. Both preserve the structure of coadjointorbit, that is the space of symmetric tri-diagonal matrices and arise as a newtype of multiplicative noise and nonlinear dissipation of the Toda lattice. Weinvestigate some of the properties of these deformations and in particular thecontinuum limit as a stochastic Burger equation with a nonlinear viscosity.This work is meant to be exploratory, and open more questions that we cananswer with simple mathematical tools and without numerical simulations.

  • JOURNAL ARTICLE
    Arnaudon A, Ganaba N, Holm DD, 2018,

    The stochastic energy-Casimir method

    , Comptes Rendus Mécanique, Vol: 346, Pages: 279-290, ISSN: 1631-0721
  • JOURNAL ARTICLE
    Arnaudon A, Holm D, Sommer S, 2018,

    String methods for stochastic image and shape matching

    , Journal of Mathematical Imaging and Vision, Vol: 60, Pages: 953-967, ISSN: 0924-9907

    Matching of images and analysis of shape differences is traditionally pursued by energy minimization of paths of deformations acting to match the shape objects. In the large deformation diffeomorphic metric mapping (LDDMM) framework, iterative gradient descents on the matching functional lead to matching algorithms informally known as Beg algorithms. When stochasticity is introduced to model stochastic variability of shapes and to provide more realistic models of observed shape data, the corresponding matching problem can be solved with a stochastic Beg algorithm, similar to the finite-temperature string method used in rare event sampling. In this paper, we apply a stochastic model compatible with the geometry of the LDDMM framework to obtain a stochastic model of images and we derive the stochastic version of the Beg algorithm which we compare with the string method and an expectation-maximization optimization of posterior likelihoods. The algorithm and its use for statistical inference is tested on stochastic LDDMM landmarks and images.

  • JOURNAL ARTICLE
    Arnaudon A, Holm DD, Sommer S, 2018,

    A Geometric Framework for Stochastic Shape Analysis

    , Foundations of Computational Mathematics, ISSN: 1615-3375

    © 2018, The Author(s). We introduce a stochastic model of diffeomorphisms, whose action on a variety of data types descends to stochastic evolution of shapes, images and landmarks. The stochasticity is introduced in the vector field which transports the data in the large deformation diffeomorphic metric mapping framework for shape analysis and image registration. The stochasticity thereby models errors or uncertainties of the flow in following the prescribed deformation velocity. The approach is illustrated in the example of finite-dimensional landmark manifolds, whose stochastic evolution is studied both via the Fokker–Planck equation and by numerical simulations. We derive two approaches for inferring parameters of the stochastic model from landmark configurations observed at discrete time points. The first of the two approaches matches moments of the Fokker–Planck equation to sample moments of the data, while the second approach employs an expectation-maximization based algorithm using a Monte Carlo bridge sampling scheme to optimise the data likelihood. We derive and numerically test the ability of the two approaches to infer the spatial correlation length of the underlying noise.

  • JOURNAL ARTICLE
    Biffi C, de Marvao A, Attard MI, Dawes TJW, Whiffin N, Bai W, Shi W, Francis C, Meyer H, Buchan R, Cook SA, Rueckert D, O'Regan DPet al., 2018,

    Three-dimensional cardiovascular imaging-genetics: a mass univariate framework

    , BIOINFORMATICS, Vol: 34, Pages: 97-103, ISSN: 1367-4803
  • JOURNAL ARTICLE
    Dawes TJW, Cai J, Quinlan M, de Marvao A, Ostrowski PJ, Tokarczuk PF, Watson GMJ, Wharton J, Howard LSGE, Gibbs JSR, Cook SA, Wilkins MR, O'Regan DPet al., 2018,

    Fractal Analysis of Right Ventricular Trabeculae in Pulmonary Hypertension

    , RADIOLOGY, Vol: 288, Pages: 386-395, ISSN: 0033-8419
  • JOURNAL ARTICLE
    Kendall ML, Ayabina P, Xu Y, Stimson J, Colijn Cet al., 2018,

    Estimating Transmission from Genetic and Epidemiological Data: A Metric to Compare Transmission Trees

    , Statistical Science, Vol: 33, Pages: 70-85, ISSN: 0883-4237

    Reconstructing who infected whom is a central challenge in analysing epidemiological data. Recently, advances in sequencing technology have led to increasing interest in Bayesian approaches to inferring who infected whom using genetic data from pathogens. The logic behind such approaches is that isolates that are nearly genetically identical are more likely to have been recently transmitted than those that are very different. A number of methods have been developed to perform this inference. However, testing their convergence, examining posterior sets of transmission trees and comparing methods’ performance are challenged by the fact that the object of inference—the transmission tree—is a complicated discrete structure. We introduce a metric on transmission trees to quantify distances between them. The metric can accommodate trees with unsampled individuals, and highlights differences in the source case and in the number of infections per infector. We illustrate its performance on simple simulated scenarios and on posterior transmission trees from a TB outbreak. We find that the metric reveals where the posterior is sensitive to the priors, and where collections of trees are composed of distinct clusters. We use the metric to define median trees summarising these clusters. Quantitative tools to compare transmission trees to each other will be required for assessing MCMC convergence, exploring posterior trees and benchmarking diverse methods as this field continues to mature.

  • JOURNAL ARTICLE
    Liu D, Mannan AA, Han Y, Oyarzún DA, Zhang Fet al., 2018,

    Dynamic metabolic control: towards precision engineering of metabolism.

    , J Ind Microbiol Biotechnol, Vol: 45, Pages: 535-543

    Advances in metabolic engineering have led to the synthesis of a wide variety of valuable chemicals in microorganisms. The key to commercializing these processes is the improvement of titer, productivity, yield, and robustness. Traditional approaches to enhancing production use the "push-pull-block" strategy that modulates enzyme expression under static control. However, strains are often optimized for specific laboratory set-up and are sensitive to environmental fluctuations. Exposure to sub-optimal growth conditions during large-scale fermentation often reduces their production capacity. Moreover, static control of engineered pathways may imbalance cofactors or cause the accumulation of toxic intermediates, which imposes burden on the host and results in decreased production. To overcome these problems, the last decade has witnessed the emergence of a new technology that uses synthetic regulation to control heterologous pathways dynamically, in ways akin to regulatory networks found in nature. Here, we review natural metabolic control strategies and recent developments in how they inspire the engineering of dynamically regulated pathways. We further discuss the challenges of designing and engineering dynamic control and highlight how model-based design can provide a powerful formalism to engineer dynamic control circuits, which together with the tools of synthetic biology, can work to enhance microbial production.

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