Statistics and Data Analytics

Staff

Dr Andrej Aderhold : Research Associate

Supervisor: Dirk Husmeier

  • Publications
  • Dr Craig Alexander : Lecturer

     

    Research student: Peter Radvanyi

  • Personal Website
  • Dr Linda Altieri : Environmental Research Associate

    Dr Craig Anderson : Lecturer

    Research students: Alison Smith, Xueqing Yin, Riham Ismail, Kamol Sanittham, Michael Waltenberger

  • Personal Website
  • Dr Mitchum Bock : Lecturer

  • Publications
  • Dr Agnieszka Borowska : Research Assistant

    Supervisor: Dirk Husmeier

  • Prof Adrian Bowman : Professor of Statistics

    Research students: Yinuo Liu, George Vazanellis

  • Personal Website
  • Publications
  • Dr Daniela Castro-Camilo : Lecturer

    Research student: Daniela Cuba

  • Personal Website
  • Dr Charalampos Chanialidis : Lecturer

  • Personal Website
  • Publications
  • Dr Christina A Cobbold : Reader

    Population dynamics of ecological systems; spatial ecology; evolutionary ecology in changing environments

    Member of other research groups: Mathematical Biology
    Research student: Renato Andrade

  • Personal Website
  • Publications
  • Dr Nema Dean : Lecturer

    Supervised and unsupervised learning; mixture models; variable selection; educational testing data; dynamic treatment regime estimation

    Research students: Shuhrah Alghamdi, Riham Ismail, Sebastian Martinez Bustos, Robin Muegge, Aldawarsi Bashayr, Alastair Gemmell

  • Personal Website
  • Publications
  • Dr Amira Elayouty : Lecturer

  • Ludger Evers : Lecturer (part-time)

    Research students: Benjamin Szili, Ivona Voroneckaja, Shuhrah Alghamdi, Dimitra Eleftheriou

  • Publications
  • Prof James Campbell Gemmell : Honorary Professor

    Prof Gemmell is chief executive of the Environment Protection Agency of South Australia.

  • Personal Website
  • Dr Mayetri Gupta : Reader

    Research students: Flynn Gewirtz-O'Reilly, Lanxin Li, Kannat Na Bangchang
    Postgraduate opportunities: Bayesian statistical data integration of single-cell and bulk “OMICS” datasets with clinical parameters for accurate prediction of treatment outcomes in Rheumatoid Arthritis, Bayesian variable selection for genetic and genomic studies

  • Personal Website
  • Publications
  • Prof Dirk Husmeier : Chair of Statistics

    Machine learning and Bayesian statistics applied to systems biology and bioinformatics; Bayesian networks; statistical phylogenetics

    Research staff: Andrej Aderhold, Agnieszka Borowska, Benn Macdonald
    Research students: Shaykah Aldossari, Aldawarsi Bashayr, Dalton David, Alan Lazarus, Luisa Paun, Campioni Nazareno, Ionut Paun, Yalei Yang
    Postgraduate opportunities: Assessing risk of heart failure with cardiac modelling and statistical inference

  • Personal Website
  • Publications
  • Prof Janine Illian : Chair/Professor in Statistical Science

    Research students: Andrew Seaton, Stephen Jun Villejo

  • Dr Eilidh Jack : Lecturer

    Research student: Robin Muegge

  • Prof Duncan Lee : Professor

    Spatiotemporal modelling; Bayesian methods; environmental epidemiology and disease mapping

    Research students: George Gerogiannis, Kamol Sanittham, Michael Waltenberger, Robin Muegge, Yoana Napier, Xueqing Yin

  • Personal Website
  • Publications
  • Dr Marnie Low : Lecturer

    Research student: Peter Radvanyi

  • Personal Website
  • Publications
  • Dr Vincent Macaulay : Reader

    Statistical genetics; population genetics; Bayesian methods; phylogenetics; GPs

    Research student: Laura Stewart

  • Personal Website
  • Publications
  • Dr Benn Macdonald : Research Assistant

    Member of other research groups: Mathematical Biology
    Research student: Hanadi Alzahrani
    Supervisor: Dirk Husmeier

  • Dr Colette Mair : Lecturer

  • Prof Claire Miller (née Ferguson): Professor

    Environmental and ecological modelling; nonparametric smoothing; time series analysis; functional data analysis

    Research students: Peter Radvanyi, Jafet Belmont Osuna, Michael Currie

  • Personal Website
  • Publications
  • Dr Gary Napier : Lecturer

    Research students: Catherine Holland, Michael Waltenberger

  • Publications
  • Dr Tereza Neocleous : Lecturer

    Forensic statistics; quantile regression; semiparametric models; biostatistics applications

    Research students: Dimitra Eleftheriou, Catherine Holland

  • Personal Website
  • Publications
  • Dr Mu Niu : Lecturer

    Research student: Wenhui Zhang

  • Dr Agostino Nobile : Honorary Research Fellow

    Bayesian statistics; MCMC and other Monte Carlo methods; mixture models; discrete choice models

  • Personal Website
  • Publications
  • Dr Ruth O'Donnell : Lecturer

  • Publications
  • Dr Theo Papamarkou : Lecturer

    Research students: Benjamin Szili, Dimitra Eleftheriou

  • Dr Surajit Ray : Senior lecturer

    COVID Resarch, Functional Data Analysis; Analysis of mixture models; high-dimensional data; medical image analysis; analysis of earth systems data; immunoinformatics

    Research students: Maryam Al Alawi , Salihah Alghamdi, Yangsong Cheng, Alastair Gemmell, Bader Lafi Q Alruwaili, Wenhui Zhang, Flynn Gewirtz-O'Reilly
    Postgraduate opportunities: Robust COVID Mortality score using Demographics, lab results and Imaging, Statistical Analyis of Medical images: Application to tumour detetection from PET imaging, Modality of mixtures of distributions, Analysis of Spatially correlated functional data objects.

  • Personal Website
  • Publications
  • Prof Marian Scott OBE: Professor of Environmental Statistics

    Radio-carbon and cosmogenic dating-design and analysis of proficiency trials; environmental radioactivity; sensitivity and uncertainty analysis applied to complex environmental models; spatial and spatiotemporal modeling of water quality; flood risk modeling; environmental indicators; developing the evidence base for environmental policy and regulation

    Research students: Jafet Belmont Osuna, Michael Currie, Yoana Napier, Daniela Cuba

  • Personal Website
  • Publications
  • Qingying Shu : Postdoctoral Research Fellow

    Supervisor: Xiaoyu Luo

  • Dr Ron Smith : Honorary Senior Research Fellow

  • Personal Website
  • Publications
  • Dr Ben Swallow : Lecturer

    Bayesian statistical inference; Markov chain Monte Carlo (MCMC) methods; data integration; model selection; stochastic processes

    Member of other research groups: Mathematical Biology
    Research student: Stephen Jun Villejo

  • Personal Website
  • Prof Michael Titterington : Honorary Senior Research Fellow

    Statistical analysis of mixture distributions; latent structure analysis; pattern recognition; machine learning; smoothing and nonparametric statistics; optimum design of experiments

  • Personal Website
  • Publications
  • Dr Bernard Torsney : Honorary Research Fellow

    Non-parametric inference; optimisation; optimal experimental design; sampling theory; applications in economics; multiple comparisons

  • Personal Website
  • Publications
  • Dr Liberty Vittert : Mitchell Lecturer

  • Personal Website
  • Dr Vlad Vyshemirsky : Lecturer

    Research student: Lida Mavrogonatou

  • Publications
  • Dr Xiaochen Yang : Lecturer

    Supervised learning; distance metric learning; hyperspectral image analysis

  • Dr Wei Zhang : Lecturer

    Ecological modelling; hierarchical models; likelihood approximation methods

    Member of other research groups: Continuum Mechanics - Modelling and Analysis of Material Systems


  • Postgraduates

    Maryam Al Alawi : PhD Student

    Supervisor: Surajit Ray

  • Salihah Alghamdi : PhD Student

    Research Topic: Analysis of Spatially correlated functional data objects.
    Supervisor: Surajit Ray

  • Jafet Belmont Osuna : PhD Student

    Supervisors: Marian Scott OBE, Claire Miller (née Ferguson)

  • Yangsong Cheng : PhD Student

    Research Topic: Computing, Inference and Applications of Hierarchical Mode Association Clustering
    Supervisor: Surajit Ray

  • Daniela Cuba : PhD Student

    Research Topic: Statistical tools to interpret soil variation
    Supervisors: Daniela Castro-Camilo, Marian Scott OBE

  • Michael Currie : PhD Student

    Supervisors: Marian Scott OBE, Claire Miller (née Ferguson)

  • Dimitra Eleftheriou : PhD Student

    Supervisors: Tereza Neocleous, Ludger Evers, Theo Papamarkou

  • Flynn Gewirtz-O'Reilly : PhD Student

    Supervisors: Mayetri Gupta, Surajit Ray

  • Catherine Holland : PhD Student

    Research Topic: Bayesian approaches to compositional data with structural zeros
    Supervisors: Gary Napier, Tereza Neocleous

  • Bader Lafi Q Alruwaili : PhD Student

    Research Topic: Clustering and Cluster Inference of complex data structures
    Supervisor: Surajit Ray

  • Yinuo Liu : PhD Student

    Supervisor: Adrian Bowman

  • Lida Mavrogonatou : PhD Student

    Supervisor: Vlad Vyshemirsky

  • Robin Muegge : PhD Student

    Research Topic: Estimating the effects of air pollution on human health
    Supervisors: Nema Dean, Duncan Lee, Eilidh Jack

  • Kannat Na Bangchang : PhD Student

    Supervisors: Mayetri Gupta, Manuele Leonelli

  • Yoana Napier : PhD Student

    Supervisors: Marian Scott OBE, Duncan Lee

  • Luisa Paun : PhD Student

    Supervisor: Dirk Husmeier

  • Peter Radvanyi : PhD Student

    Research Topic: Groundwater monitoring design
    Supervisors: Claire Miller (née Ferguson), Craig Alexander, Marnie Low

  • Kamol Sanittham : PhD Student

    Supervisors: Duncan Lee, Craig Anderson

  • Andrew Seaton : PhD Student

    Research Topic: Spatial Point Process Models
    Supervisor: Janine Illian

  • Alison Smith : PhD Student

    Research Topic: Developing novel ways to represent spatial patterns in disease risk
    Supervisor: Craig Anderson

  • Laura Stewart : PhD Student

    Research Topic: Development and application of stochastic models of agglomeration
    Supervisors: Vincent Macaulay, Alexey Lindo

  • Benjamin Szili : PhD Student

    Supervisors: Ludger Evers, Theo Papamarkou

  • George Vazanellis : PhD Student

    Research Topic: Spatiotemporal models for environmental data
    Supervisor: Adrian Bowman

  • Stephen Jun Villejo : PhD Student

    Research Topic: A Bayesian Spatio-Temporal Model to Test for Stability of Risks for Spatially Misaligned Data
    Supervisors: Ben Swallow, Janine Illian

  • Ivona Voroneckaja : PhD Student

    Supervisor: Ludger Evers

  • Michael Waltenberger : PhD Student

    Supervisors: Duncan Lee, Craig Anderson, Gary Napier

  • Yalei Yang : PhD Student

    Supervisors: Hao Gao, Dirk Husmeier

  • Xueqing Yin : PhD Student

    Research Topic: Mapping disease risk in space and time
    Supervisors: Craig Anderson, Duncan Lee

  • Wenhui Zhang : PhD Student

    Research Topic: Analysis of Positron Emission Tomography data for tumour detection and delineation
    Supervisors: Surajit Ray, Mu Niu


  • Postgraduate opportunities

    Bayesian variable selection for genetic and genomic studies (PhD)

    Supervisors: Mayetri Gupta
    Relevant research groups: Statistics and Data Analytics

    An important issue in high-dimensional regression problems is the accurate and efficient estimation of regression coefficients when, compared to the number of data points, a substantially larger number of potential predictors are present. Further complications arise with correlated predictors, leading to the breakdown of standard statistical models for inference; and the uncertain definition of the outcome variable, which is often a varying composition of several different observable traits. Examples of such problems arise in many scenarios in genomics- in determining expression patterns of genes that may be responsible for a type of cancer; and in determining which genetic mutations lead to higher risks for occurrence of a disease. This project involves developing broad and improved Bayesian methodologies for efficient inference in high-dimensional regression-type problems with complex outcomes, with a focus on genetic data applications. Further, we will extend this framework to a variety of latent class models, and investigate the operating characteristics and analytical properties of various priors in the context of high-dimensional variable selection problems.

    The successful candidate should have a strong background in methodological and applied Statistics, expert skills in relevant statistical software or programming languages (such as R, C/C++/Python), and also have a deep interest in developing knowledge in cross-disciplinary topics in genomics. The candidate will be expected to consolidate and master an extensive range of topics in modern Statistical theory and applications during their PhD, including advanced Bayesian modelling and computation, latent variable models, machine learning, and methods for Big Data. The successful candidate will be considered for funding to cover domestic tuition fees, as well as paying a stipend at the Research Council rate for four years.

     

    Analysis of Spatially correlated functional data objects. (PhD)

    Supervisors: Surajit Ray
    Relevant research groups: Statistics and Data Analytics

    Historically, functional data analysis techniques have widely been used to analyze traditional time series data, albeit from a different perspective. Of late, FDA techniques are increasingly being used in domains such as environmental science, where the data are spatio-temporal in nature and hence is it typical to consider such data as functional data where the functions are correlated in time or space. An example where modeling the dependencies is crucial is in analyzing remotely sensed data observed over a number of years across the surface of the earth, where each year forms a single functional data object. One might be interested in decomposing the overall variation across space and time and attribute it to covariates of interest. Another interesting class of data with dependence structure consists of weather data on several variables collected from balloons where the domain of the functions is a vertical strip in the atmosphere, and the data are spatially correlated. One of the challenges in such type of data is the problem of missingness, to address which one needs develop appropriate spatial smoothing techniques for spatially dependent functional data. There are also interesting design of experiment issues, as well as questions of data calibration to account for the variability in sensing instruments. Inspite of the research initiative in analyzing dependent functional data there are several unresolved problems, which the student will work on:

    • robust statistical models for incorporating temporal and spatial dependencies in functional data
    • developing reliable prediction and interpolation techniques for dependent functional data
    • developing inferential framework for testing hypotheses related to simplified dependent structures
    • analysing sparsely observed functional data by borrowing information from neighbours
    • visualisation of data summaries associated with dependent functional data
    • Clustering of functional data

     

    Predicting patterns of retinal haemorrhage (PhD)

    Supervisors: Peter Stewart
    Relevant research groups: Continuum Mechanics - Fluid Dynamics and Magnetohydrodynamics, Continuum Mechanics - Modelling and Analysis of Material Systems, Mathematical Biology, Statistics and Data Analytics

    Retinal haemorrhage (bleeding of the blood vessels in the retina) often accompanies traumatic brain injury and is one of the clinical indicators of `shaken baby syndrome'. This PhD project will give you the opportunity to develop a combination of mathematical and statistical models to help explain the onset of retinal haemorrhage. You will devise and implement image processing algorithms to quantify the pattern of bleeding in clinical images of haemorrhaged retinas. In addition, you will develop a mathematical model for pressure wave propagation through the retinal circulation in response to an acute rise in intracranial pressure, to predict the pattern of retinal bleeding and correlate to the images. Cutting-edge pattern recognition methods from Machine Learning and Bayesian modelling will be used to infer characteristic signatures of different types of brain trauma. These will be used to help clinicians in characterising the origin of traumatic brain injury and diagnosing its severity. This is an ideal project for a postgraduate student with an interest in applying mathematical modelling, image analysis and machine learning to predictive healthcare. The project will give you the opportunity to join a cross-disciplinary Research Hub that aims to push the boundaries of quantitative medicine and improve clinical decision making in cases of suspected non-accidental head injury using innovative mathematical and statistical modelling.

     

    Assessing risk of heart failure with cardiac modelling and statistical inference (PhD)

    Supervisors: Dirk Husmeier, Hao Gao, Xiaoyu Luo
    Relevant research groups: Mathematical Biology, Statistics and Data Analytics

    In recent years, we have witnessed impressive developments in the mathematical modelling of complex physiological systems. However, parameter estimation and uncertainty quantification still remain challenging. This PhD project will give you the opportunity to join an interdisciplinary research team to develop new methodologies for computational modelling and inference in cardio-mechanic models. Your ultimate objective will be to contribute to paving the path to a new generation of clinical decision support systems for cardiac disease risk assessment based on complex mathematical-physiological models. You will aim to  achieve patient-specific calibration of these models in real time, using magnetic resonance imaging data. Sound uncertainty quantification for informed risk assessment will be paramount. This is an ideal PhD project for a postgraduate student with a strong applied mathematics and statistics or Computer Science background who is interested in computational mechanics and adapting cutting-edge inference and pattern recognition methods from Machine Learning and Bayesian modelling to challenging cardio-mechanic modelling problems. The project will give you the opportunity to join a cross-disciplinary Research Hub that aims to push the boundaries of quantitative medicine and improve cardio-vascular healthcare by bringing cutting-edge mathematical and statistical modelling into the clinic.

     

    Modality of mixtures of distributions (PhD)

    Supervisors: Surajit Ray
    Relevant research groups: Statistics and Data Analytics

    Finite mixtures provide a flexible and powerful tool for fitting univariate and multivariate distributions that cannot be captured by standard statistical distributions. In particular, multivariate mixtures have been widely used to perform modeling and cluster analysis of high-dimensional data in a wide range of applications. Modes of mixture densities have been used with great success for organizing mixture components into homogenous groups. But the results are limited to normal mixtures. Beyond the clustering application existing research in this area has provided fundamental results regarding the upper bound of the number of modes, but they too are limited to normal mixtures. In this project, we wish to explore the modality of non-normal distributions and their application to real life problems

     

     

    Statistical Analyis of Medical images: Application to tumour detetection from PET imaging (PhD)

    Supervisors: Surajit Ray
    Relevant research groups: Statistics and Data Analytics

    Positron-emission tomography (PET) is a nuclear medicine functional imaging technique that is used to observe metabolic processes in the body and is often used for tumour detection. Unlike CT or MRI scans PET scans are more reliable as the target the metabolic process but are very expensive. There are only 5 PET scanners in the whole of Scotland and around 30 in England. Further, very limited information from the images is used by the radiologists to hand segment the tumour. It is often challenging to extract the tumour alone from the background of healthy tissues and image noise. In this project, we will explore existing methods for automatic segmentation of tumor based on PET images and develop a technique to implement automatic segmentation on anonymized PET images obtained at Gartnavel Hospital.

     

    Robust COVID Mortality score using Demographics, lab results and Imaging (PhD)

    Supervisors: Surajit Ray
    Relevant research groups: Statistics and Data Analytics

     

    In this project, you will develop a host of robust prediction tools for COVID-19 patient care. Our team has access to diverse in-patient data from three health boards in the UK. The novelty of this research lies in the inclusion of an extensive range of data from haematology, clinical chemistry, microbiology, virology, and medical imaging to design a visual and comprehensive clinical picture for COVID-19 patients. This will help to improve a healthcare provider’s capacity in the diagnostic process and in the management of resources. Crucially, this research will develop an app that can be used by healthcare providers immediately that will better equip health organizations with adaptive and predictive screening tools from early-onset which will control future peaks of the pandemic. Our method would allow rapid screening and prediction a week before the window for full diagnosis and enable early quarantine of predicted cases.

    The successful candidate will have the chance to work in a very dynamic academic environment offered by the Statistics research group at University of Glasgow along with a diverse team of researchers from University of Brighton, University of Lincoln and the NHS. If you are interested in a sample of our research, then please read the paper Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population. Our research is supported by EPSRC Impact Acceleration Grant through the University of Glasgow and seed grants offered through the University of Brighto.

    During the PhD the student will be expected to master a broad range of statistical and computational knowledge, including Big data, Bayesian Analysis, Functional data Analysis, Spatial Statistics and Machine learning in order to tackle the mathematical and computational challenges associated with prediction from disparate sources of data. The project provides an excellent opportunity to conduct cutting edge methodological development complemented by production of user-friendly software. The successful candidate will need to be comfortable with interfacing with professionals from other disciplines and industry partner and be passionate about their research.

     

     Funding Notes

    The successful candidate will be considered for funding to cover domestic tuition fees, as well as paying a stipend at the Research Council rate (estimated £15,609 for Session 2021-22) for four years.

     

    Bayesian statistical data integration of single-cell and bulk “OMICS” datasets with clinical parameters for accurate prediction of treatment outcomes in Rheumatoid Arthritis (PhD)

    Supervisors: Mayetri Gupta
    Relevant research groups: Mathematical Biology, Statistics and Data Analytics

    In recent years, many different computational methods to analyse biological data have been established: including DNA (Genomics), RNA (Transcriptomics), Proteins (proteomics) and Metabolomics, that captures more dynamic events. These methods were refined by the advent of single cell technology, where it is now possible to capture the transcriptomics profile of single cells, spatial arrangements of cells from flow methods or imaging methods like functional magnetic resonance imaging. At the same time, these OMICS data can be complemented with clinical data – measurement of patients, like age, smoking status, phenotype of disease or drug treatment. It is an interesting and important open statistical question[1] how to combine data from different “modalities” (like transcriptome with clinical data or imaging data) in a statistically valid way, to compare different datasets and make justifiable statistical inferences.

    In this PhD project (jointly supervised with Dr. Thomas Otto and Prof. Stefan Siebert from the Institute of Infection, Immunity & Inflammation), you will explore how to combine different datasets using Bayesian latent variable modelling, focusing on clinical datasets from Rheumatoid Arthritis. Single cell data has been generated from rheumatoid arthritis patients[2] from synovial and blood samples. This will be combined with rich clinical datasets from cohorts like SERA [3], RAMAP[4] and others, that all have transcriptomics data (bulk RNA-Seq from blood and some tissues). All these datasets are already curated and stored in a tranSMART database. In the different datasets, the patients were treated with different drugs and their response or the lack of it, was recorded over time.

    Our overall aim is to build a Bayesian statistical framework and methodology that can combine these different data types in a latent space in a statistically justifiable way, with the goal of more accurate prediction of clinical outcomes than can be achieved with a single (or fewer types of) dataset alone.  The secondary aim is to develop robust and efficient Bayesian computational methodologies to fit these models on ultra-high-dimensional, complex datasets to make valid inferences, build user-friendly, publicly available computational software (in R) implementing these methods, and compare them to other currently available computational tools, both in simulated and real datasets.

    Some questions of interest are: (1) determining if it is possible to differentiate from the single cell data the different phenotypes (active RA, remission) in the clinical data; (2) explore if in the latent space, it is possible to combine the different modalities when including further datasets from the IMID-Bio-UK dataset as well as imaging data; (3) exploring our methods in the context of Rheumatoid Arthritis with Psoriasis Arthritis- which are two immune mediated inflammatory diseases with distinct pathways but also similarities- can our proposed methods (a) confirm existing findings (b) highlight novel shared signatures between the two diseases?

    Applicant criteria

    The successful candidate should have a strong training and background in theoretical, methodological and applied Statistics, expert skills in relevant statistical software or programming languages (such as R, Python/C/C++, or MATLab), and also have a deep interest in developing knowledge in cross-disciplinary topics in genomics, sequencing technology, and inflammatory disease, during the PhD. The candidate will be expected to consolidate and master an extensive range of topics in modern Statistical theory and applications during their PhD, including advanced Bayesian modelling and computation, latent variable models, machine learning, and methods for Big Data. The candidate is expected to have excellent interpersonal and communication skills (oral and written) and to be enthusiastic and comfortable interacting and communicating with researchers in other disciplines, especially in biology and medicine.

    Funding Notes

    The successful candidate will be considered for funding to cover domestic tuition fees, as well as paying a stipend at the Research Council rate (estimated £15,609 for Session 2021-22) for four years.

    References:

    1. Adossa N,  Khan S, Rytkönen KT, Elo LL: Computational strategies for single-cell multi-omics integration. Comput. Struct. Biotechnol 2021, 19: 2588-2596.
    2. Alivernini S, MacDonald L, Elmesmari A, Finlay S, Tolusso B, Gigante MR, Petricca L, Di Mario C, Bui L, Perniola S et al: Distinct synovial tissue macrophage subsets regulate inflammation and remission in rheumatoid arthritis. Nat Med 2020, 26(8):1295-1306.
    3. Dale J, Paterson C, Tierney A, Ralston SH, Reid DM, Basu N, Harvie J, McKay ND, Saunders S, Wilson H et al: The Scottish Early Rheumatoid Arthritis (SERA) Study: an inception cohort and biobank. BMC Musculoskelet Disord 2016, 17(1):461.
    4. Cope AP, Barnes MR, Belson A, Binks M, Brockbank S, Bonachela-Capdevila F, Carini C, Fisher BA, Goodyear CS, Emery P et al: The RA-MAP Consortium: a working model for academia-industry collaboration. Nat Rev Rheumatol 2018, 14(1):53-60.