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Publications from our Researchers

Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.  

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  • Journal article
    Oehmichen A, Hua K, Diaz Lopez JA, Molina-Solana M, Gomez-Romero J, Guo Y-Ket al., 2019,

    Not All Lies Are Equal. A Study Into the Engineering of Political Misinformation in the 2016 US Presidential Election

    , IEEE ACCESS, Vol: 7, Pages: 126305-126314, ISSN: 2169-3536
  • Journal article
    de Montjoye Y-A, Gambs S, Blondel V, Canright G, de Cordes N, Deletaille S, Engø-Monsen K, Garcia-Herranz M, Kendall J, Kerry C, Krings G, Letouzé E, Luengo-Oroz M, Oliver N, Rocher L, Rutherford A, Smoreda Z, Steele J, Wetter E, Pentland AS, Bengtsson Let al., 2018,

    On the privacy-conscientious use of mobile phone data

    , Scientific Data, Vol: 5, ISSN: 2052-4463

    The breadcrumbs we leave behind when using our mobile phones—who somebody calls, for how long, and from where—contain unprecedented insights about us and our societies. Researchers have compared the recent availability of large-scale behavioral datasets, such as the ones generated by mobile phones, to the invention of the microscope, giving rise to the new field of computational social science.
  • Journal article
    Gomez-Romero J, Molina-Solana MJ, Oehmichen A, Guo Yet al., 2018,

    Visualizing large knowledge graphs: a performance analysis

    , Future Generation Computer Systems, Vol: 89, Pages: 224-238, ISSN: 0167-739X

    Knowledge graphs are an increasingly important source of data and context information in Data Science. A first step in data analysis is data exploration, in which visualization plays a key role. Currently, Semantic Web technologies are prevalent for modelling and querying knowledge graphs; however, most visualization approaches in this area tend to be overly simplified and targeted to small-sized representations. In this work, we describe and evaluate the performance of a Big Data architecture applied to large-scale knowledge graph visualization. To do so, we have implemented a graph processing pipeline in the Apache Spark framework and carried out several experiments with real-world and synthetic graphs. We show that distributed implementations of the graph building, metric calculation and layout stages can efficiently manage very large graphs, even without applying partitioning or incremental processing strategies.
  • Journal article
    Molina-Solana M, Kennedy M, Amador Diaz Lopez J, 2018,

    foo.castr: visualising the future AI workforce

    , Big Data Analytics, Vol: 3, ISSN: 2058-6345

    Organization of companies and their HR departments are becoming hugely affected by recent advancements in computational power and Artificial Intelligence, with this trend likely to dramatically rise in the next few years. This work presents foo.castr, a tool we are developing to visualise, communicate and facilitate the understanding of the impact of these advancements in the future of workforce. It builds upon the idea that particular tasks within job descriptions will be progressively taken by computers, forcing the shaping of human jobs. In its current version, foo.castr presents three different scenarios to help HR departments planning potential changes and disruptions brought by the adoption of Artificial Intelligence.
  • Journal article
    Brandsma J, Goss VM, Yang X, Bakke PS, Caruso M, Chanez P, Dahlen S-E, Fowler SJ, Horvath I, Krug N, Montuschi P, Sanak M, Sandstrom T, Shaw DE, Chung KF, Singer F, Fleming LJ, Sousa AR, Pandis I, Bansal AT, Sterk PJ, Djukanovic R, Postle ADet al., 2018,

    Lipid phenotyping of lung epithelial lining fluid in healthy human volunteers

    , Metabolomics, Vol: 14, ISSN: 1573-3882

    BackgroundLung epithelial lining fluid (ELF)—sampled through sputum induction—is a medium rich in cells, proteins and lipids. However, despite its key role in maintaining lung function, homeostasis and defences, the composition and biology of ELF, especially in respect of lipids, remain incompletely understood.ObjectivesTo characterise the induced sputum lipidome of healthy adult individuals, and to examine associations between different ELF lipid phenotypes and the demographic characteristics within the study cohort.MethodsInduced sputum samples were obtained from 41 healthy non-smoking adults, and their lipid compositions analysed using a combination of untargeted shotgun and liquid chromatography mass spectrometry methods. Topological data analysis (TDA) was used to group subjects with comparable sputum lipidomes in order to identify distinct ELF phenotypes.ResultsThe induced sputum lipidome was diverse, comprising a range of different molecular classes, including at least 75 glycerophospholipids, 13 sphingolipids, 5 sterol lipids and 12 neutral glycerolipids. TDA identified two distinct phenotypes differentiated by a higher total lipid content and specific enrichments of diacyl-glycerophosphocholines, -inositols and -glycerols in one group, with enrichments of sterols, glycolipids and sphingolipids in the other. Subjects presenting the lipid-rich ELF phenotype also had significantly higher BMI, but did not differ in respect of other demographic characteristics such as age or gender.ConclusionsWe provide the first evidence that the ELF lipidome varies significantly between healthy individuals and propose that such differences are related to weight status, highlighting the potential impact of (over)nutrition on lung lipid metabolism.
  • Journal article
    Dolan D, Jensen H, Martinez Mediano P, Molina-Solana MJ, Rajpal H, Rosas De Andraca F, Sloboda JAet al., 2018,

    The improvisational state of mind: a multidisciplinary study of an improvisatory approach to classical music repertoire performance

    , Frontiers in Psychology, Vol: 9, ISSN: 1664-1078

    The recent re-introduction of improvisation as a professional practice within classical music, however cautious and still rare, allows direct and detailed contemporary comparison between improvised and “standard” approaches to performances of the same composition, comparisons which hitherto could only be inferred from impressionistic historical accounts. This study takes an interdisciplinary multi-method approach to discovering the contrasting nature and effects of prepared and improvised approaches during live chamber-music concert performances of a movement from Franz Schubert’s “Shepherd on the Rock”, given by a professional trio consisting of voice, flute, and piano, in the presence of an invited audience of 22 adults with varying levels of musical experience and training. The improvised performances were found to be differ systematically from prepared performances in their timing, dynamic, and timbral features as well as in the degree of risk-taking and “mind reading” between performers including during moments of added extemporised notes. Post-performance critical reflection by the performers characterised distinct mental states underlying the two modes of performance. The amount of overall body movements was reduced in the improvised performances, which showed less unco-ordinated movements between performers when compared to the prepared performance. Audience members, who were told only that the two performances would be different, but not how, rated the improvised version as more emotionally compelling and musically convincing than the prepared version. The size of this effect was not affected by whether or not the audience could see the performers, or by levels of musical training. EEG measurements from 19 scalp locations showed higher levels of Lempel-Ziv complexity (associated with awareness and alertness) in the improvised version in both performers and audience. Results are discussed in terms of their potential
  • Journal article
    Gómez-Romero J, Molina-Solana M, Ros M, Ruiz MD, Martin-Bautista MJet al., 2018,

    Comfort as a service: a new paradigm for residential environmental quality control

    , Sustainability, Vol: 10, ISSN: 1937-0709

    This paper introduces the concept of Comfort as a Service (CaaS), a new energy supply paradigm for providing comfort to residential customers. CaaS takes into account the available passive and active elements, the external factors that affect energy consumption and associated costs, and occupants' behaviors to generate optimal control strategies for the domestic equipment automatically. As a consequence, it releases building occupants from operating the equipment, which gives rise to a disruption of the traditional model of paying per consumed energy in favor of a model of paying per provided comfort. In the paper, we envision a realization of CaaS based on several technologies such as ambient intelligence, big data, cloud computing and predictive computing. We discuss the opportunities and the barriers of CaaS-centered business and exemplify the potential of CaaS deployments by quantifying the expected energy savings achieved after limiting occupants' control over the air conditioning system in a test scenario.
  • Journal article
    Song J, Fan S, Lin W, Mottet L, Woodward H, Wykes MD, Arcucci R, Xiao D, Debay J-E, ApSimon H, Aristodemou E, Birch D, Carpentieri M, Fang F, Herzog M, Hunt GR, Jones RL, Pain C, Pavlidis D, Robins AG, Short CA, Linden PFet al., 2018,

    Natural ventilation in cities: the implications of fluid mechanics

    , BUILDING RESEARCH AND INFORMATION, Vol: 46, Pages: 809-828, ISSN: 0961-3218
  • Journal article
    Takahashi K, Pavlidis S, Ng Kee Kwong F, Hoda U, Rossios C, Sun K, Loza M, Baribaud F, Chanez P, Fowler SJ, Horvath I, Montuschi P, Singer F, Musial J, Dahlen B, Dahlen SE, Krug N, Sandstrom T, Shaw DE, Lutter R, Bakke P, Fleming LJ, Howarth PH, Caruso M, Sousa AR, Corfield J, Auffray C, De Meulder B, Lefaudeux D, Djukanovic R, Sterk PJ, Guo Y, Adcock I, Chung KFet al., 2018,

    Sputum proteomics and airway cell transcripts of current and ex-smokers with severe asthma in U-BIOPRED: an exploratory analysis

    , European Respiratory Journal, Vol: 51, ISSN: 0903-1936

    Background: Severe asthma patients with a significant smoking history have airflow obstruction with reported neutrophilia. We hypothesise that multi1omic analysis will enable the definition of smoking and ex1smoking severe asthma molecular phenotypes.Methods The U1BIOPRED severe asthma patients containing current1smokers (CSA), ex1smokers (ESA), non1smokers (NSA) and healthy non1smokers (NH) was examined. Blood and sputum cell counts, fractional exhaled nitric oxide and spirometry were obtained. Exploratory proteomic analysis of sputum supernatants and transcriptomic analysis of bronchial brushings, biopsies and sputum cells was performed. Results Colony stimulating factor (CSF)2 protein levels were increased in CSA sputum supernatants with azurocidin 1, neutrophil elastase and CXCL8 upregulated in ESA. Phagocytosis and innate immune pathways were associated with neutrophilic inflammation in ESA. Gene Set Variation Analysis of bronchial epithelial cell transcriptome from CSA showed enrichment of xenobiotic metabolism, oxidative stress and endoplasmic reticulum stress compared to other groups. CXCL5 and matrix metallopeptidase 12 genes were upregulated in ESA and the epithelial protective genes, mucin 2 and cystatin SN, were downregulated. Conclusion Despite little difference in clinical characteristics, CSA were distinguishable from ESA subjects at the sputum proteomic level with CSA having increased CSF2 expression and ESA patients showed sustained loss of epithelial barrier processes.
  • Journal article
    Hekking PP, Loza MJ, Pavlidis S, De Meulder B, Lefaudeux D, Baribaud F, Auffray C, Wagener AH, Brinkman P, Lutter R, Bansal AT, Sousa AR, Bates S, Pandis Y, Fleming LJ, Shaw DE, Fowler SJ, Guo Y, Meiser A, Sun K, Corfield J, Howarth P, Bel EH, Adcock IM, Chung KF, Djukanovic R, Sterk PJ, U-BIOPRED Study Groupet al., 2017,

    Transcriptomic gene signatures associated with persistent airflow limitation in patients with severe asthma

    , European Respiratory Journal, Vol: 50, ISSN: 1399-3003

    Rationale:A proportion of severe asthma patients suffers from persistent airflow limitation, often associated with more symptoms and exacerbations. Little is known about the underlying mechanisms. Aiming for discovery of unexplored potential mechanisms, we used Gene Set Variation Analysis (GSVA), a sensitive technique that can detect underlying pathways in heterogeneous samples. Methods: Severe asthma patients from the U-BIOPRED cohort with persistent airflow limitation (post-bronchodilator FEV1/FVC ratio < lower limit of normal) were compared to those without persistent airflow limitation. Gene expression was assessed on the total RNA of sputum cells, nasal brushings and endobronchial brushings and biopsies. GSVA was applied to identify differentially-enriched pre-defined gene signatures based on all available gene expression publications and data on airways disease.Results: Differentially-enriched gene signatures were identified in nasal brushings (1), sputum (9), bronchial brushings (1) and bronchial biopsies (4), that were associated with response to inhaled steroids, eosinophils, IL-13, IFN-alpha, specific CD4+ T-cells and airway remodeling.Conclusion: Persistent airflow limitation in severe asthma has distinguishable underlying gene networks that are associated with treatment, inflammatory pathways and airway remodeling. These results point towards targets for the therapy of persistent airflow limitation in severe asthma.
  • Journal article
    Hekking PP, Loza MJ, Pavlidis S, De Meulder B, Lefaudeux D, Baribaud F, Auffray C, Wagener A, Brinkman P, Lutter I, Bansal A, Sousa A, Bates S, Pandis Y, Fleming L, Shaw DE, Fowler SJ, Guo Y, Meiser A, Sun K, Corfield J, Howarth P, Bel EH, Adcock IM, Chung KF, Djukanovic R, Sterk PJ, U-BIOPRED Study Groupet al., 2017,

    Pathway discovery using transcriptomic profiles in adult-onset severe asthma

    , Journal of Allergy and Clinical Immunology, Vol: 141, Pages: 1280-1290, ISSN: 1097-6825

    RationaleAdult-onset severe asthma is characterized by highly symptomatic disease despite high intensity asthma treatments. Understanding of the underlying pathways of this heterogeneous disease needed for the development of targeted treatments. Gene Set Variation Analysis (GSVA) is a statistical technique to identify gene profiles in heterogeneous samples.ObjectiveTo identify gene profiles associated with adult-onset severe asthma.MethodsThis was a cross-sectional, observational study in which adult patients with adult-onset of asthma (defined as starting at ≥18yrs old) as compared to childhood-onset severe asthma (<18 yrs) were selected from the U-BIOPRED cohort. Gene expression was assessed on the total RNA of induced sputum (n=83), nasal brushings (n=41), and endobronchial brushings (n=65) and biopsies (n=47) (Affymetrix HT HG-U133+ PM). GSVA was used to identify differentially enriched pre-defined gene signatures of leukocyte lineage, inflammatory and induced lung injury pathways.ResultsSignificant differentially enriched gene signatures in patients with adult-onset as compared to childhood-onset severe asthma were identified in nasal brushings (5 signatures), sputum (3 signatures) and endobronchial brushings (6 signatures). Signatures associated with eosinophilic airway inflammation, mast cells and group 3 innate lymphoid cells (ILC3) were more enriched in adult-onset severe asthma, whereas signatures associated with induced lung injury were less enriched in adult-onset severe asthma.ConclusionsAdult-onset severe asthma is characterized by inflammatory pathways involving eosinophils, mast cells and ILC3s. These pathways could represent useful targets for the treatment of adult-onset severe asthma.
  • Journal article
    Rossios C, Pavlidis S, Hoda U, Kuo CH, Wiegman C, Russell K, Sun K, Loza MJ, Baribaud F, Durham AL, Ojo O, Lutter R, Rowe A, Bansal A, Auffray C, Sousa A, Corfield J, Djukanovic R, Guo Y, Sterk PJ, Chung KF, Adcock IM, Unbiased Biomarkers for the Prediction of Respiratory Diseases Outcomes U-BIOPRED Consortia Project Teamet al., 2017,

    Sputum transcriptomics reveal upregulation of IL-1 receptor family members in patients with severe asthma

    , Journal of Allergy and Clinical Immunology, Vol: 141, Pages: 560-570, ISSN: 1097-6825

    BACKGROUND: Sputum analysis in asthmatic patients is used to define airway inflammatory processes and might guide therapy. OBJECTIVE: We sought to determine differential gene and protein expression in sputum samples from patients with severe asthma (SA) compared with nonsmoking patients with mild/moderate asthma. METHODS: Induced sputum was obtained from nonsmoking patients with SA, smokers/ex-smokers with severe asthma, nonsmoking patients with mild/moderate asthma (MMAs), and healthy nonsmoking control subjects. Differential cell counts, microarray analysis of cell pellets, and SOMAscan analysis of sputum analytes were performed. CRID3 was used to inhibit the inflammasome in a mouse model of SA. RESULTS: Eosinophilic and mixed neutrophilic/eosinophilic inflammation were more prevalent in patients with SA compared with MMAs. Forty-two genes probes were upregulated (>2-fold) in nonsmoking patients with severe asthma compared with MMAs, including IL-1 receptor (IL-1R) family and nucleotide-binding oligomerization domain, leucine-rich repeat and pyrin domain containing 3 (NRLP3) inflammasome members (false discovery rate < 0.05). The inflammasome proteins nucleotide-binding oligomerization domain, leucine rich repeat and pyrin domain containing 1 (NLRP1), NLRP3, and nucleotide-binding oligomerization domain (NOD)-like receptor C4 (NLRC4) were associated with neutrophilic asthma and with sputum IL-1β protein levels, whereas eosinophilic asthma was associated with an IL-13-induced TH2 signature and IL-1 receptor-like 1 (IL1RL1) mRNA expression. These differences were sputum specific because no activation of NLRP3 or enrichment of IL-1R family genes in bronchial brushings or biopsy specimens in patients with SA was observed. Expression of NLRP3 and of the IL-1R family genes was validated in the Airway Disease Endotyping for Personalized Therapeutics cohort. Inflammasome inhibition using CRID3 prevented airway hyperresponsiveness and airway inflammati
  • Journal article
    Jahani E, Sundsøy P, Bjelland J, Bengtsson L, Pentland AS, de Montjoye Y-Aet al., 2017,

    Improving official statistics in emerging markets using machine learning and mobile phone data

    , EPJ Data Science, Vol: 6, ISSN: 2193-1127

    Mobile phones are one of the fastest growing technologies in the developing world with global penetration rates reaching 90%. Mobile phone data, also called CDR, are generated everytime phones are used and recorded by carriers at scale. CDR have generated groundbreaking insights in public health, official statistics, and logistics. However, the fact that most phones in developing countries are prepaid means that the data lacks key information about the user, including gender and other demographic variables. This precludes numerous uses of this data in social science and development economic research. It furthermore severely prevents the development of humanitarian applications such as the use of mobile phone data to target aid towards the most vulnerable groups during crisis. We developed a framework to extract more than 1400 features from standard mobile phone data and used them to predict useful individual characteristics and group estimates. We here present a systematic cross-country study of the applicability of machine learning for dataset augmentation at low cost. We validate our framework by showing how it can be used to reliably predict gender and other information for more than half a million people in two countries. We show how standard machine learning algorithms trained on only 10,000 users are sufficient to predict individual’s gender with an accuracy ranging from 74.3 to 88.4% in a developed country and from 74.5 to 79.7% in a developing country using only metadata. This is significantly higher than previous approaches and, once calibrated, gives highly accurate estimates of gender balance in groups. Performance suffers only marginally if we reduce the training size to 5,000, but significantly decreases in a smaller training set. We finally show that our indicators capture a large range of behavioral traits using factor analysis and that the framework can be used to predict other indicators of vulnerability such as age or socio-economic status. M
  • Journal article
    Steele JE, Sundsoy PR, Pezzulo C, Alegana VA, Bird TJ, Blumenstock J, Bjelland J, Engo-Monsen K, de Montjoye YKJV, Iqbal AM, Hadiuzzaman KN, Lu X, Wetter E, Tatem AJ, Bengtsson Let al., 2017,

    Mapping poverty using mobile phone and satellite data

    , Journal of the Royal Society Interface, Vol: 14, ISSN: 1742-5689

    Poverty is one of the most important determinants of adverse health outcomesglobally, a major cause of societal instability and one of the largest causes of losthuman potential. Traditional approaches to measuring and targeting povertyrely heavily on census data, which in most low- and middle-income countries(LMICs) are unavailable or out-of-date.Alternate measures are needed to comp-lement and update estimates between censuses. This study demonstrates howpublic and private data sources that are commonly available for LMICs can beused to provide novel insight into the spatial distribution of poverty. We evalu-ate the relative value of modelling three traditional poverty measures usingaggregate data from mobile operators and widely available geospatial data.Taken together, models combining these data sources providethebest predictivepower (highestr2¼0.78) and lowest error, but generally models employingmobile data only yield comparable results, offering the potential to measurepoverty more frequently and at finer granularity. Stratifying models intourban and rural areas highlights the advantage of using mobile data in urbanareas and different data in different contexts. The findings indicate the possibilityto estimate and continually monitor poverty rates at high spatial resolution incountries with limited capacity to support traditional methods of datacollection.
  • Journal article
    Molina-Solana MJ, Guo Y, Birch D, 2017,

    Improving data exploration in graphs with fuzzy logic and large-scale visualisation

    , Applied Soft Computing, Vol: 53, Pages: 227-235, ISSN: 1872-9681

    This work presents three case-studies of how fuzzy logic can be combined with large-scale immersive visualisation to enhance the process of graph sensemaking, enabling interactive fuzzy filtering of large global views of graphs. The aim is to provide users a mechanism to quickly identify interesting nodes for further analysis. Fuzzy logic allows a flexible framework to ask human-like curiosity-driven questions over the data, and visualisation allows its communication and understanding. Together, these two technologies successfully empower novices and experts to a faster and deeper understanding of the underlying patterns in big datasets compared to traditional means in a desktop screen with crisp queries. Among other examples, we provide evidence of how these two technologies successfully enable the identification of relevant transaction patterns in the Bitcoin network.

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