NIHRDataInsights/HRCSData huggingface dataset:
Title: WormBase: expanding the reference resource for helminth research
Funder: Medical Research Council

Abstract: We will integrate, curate, analyse and disseminate (through the community website wormbase.org) molecular and other research data from C. elegans - a widely used model organism - and eight important species of parasitic worms. Our approach is to import (especially sequence-based) data from relevant public archives, analyse it automatically using state-of-the-art tools (particularly for the structural and functional annotation of reference genome sequences), to revise these data by manual review and to supplement them by abstraction of functional information and experimental metadata from the scientific literature into ontological vocabularies that can be used for semantic querying. We will support this process using a mixture of community-standard and in-house bioinformatics tools and technologies, including the WormBase database infrastructure (which is in the process of migrating to a new backend using the Datomic database); the Apollo annotation tool; InterProScan for annotation of protein domains; the Gene Ontology and other, worm-specific ontologies; in-house software for literature-identification and triage; state-of-the-art tools (e.g. Kallisto) for RNA alignment. We will use common formats (e.g. FASTA, GFF, BAM/CRAM, VCF, track and assembly hubs) to collect, share and disseminate data according to type. We will scan the molecular archives to automatically identify important new data sets, and additionally keep in regular communication with members of the research community to ensure that no recent or forthcoming data is overlooked. Where requested, we will help community members prepare new data for submission to the public archives. We will coordinate these efforts with other projects of the UK WormBase team, and with our international partners on the WormBase project, managing a series of regular data releases in an efficient fashion and making a significant contribution from the UK to the global development of the WormBase resource.


Research activity codes: 1.1 NORMAL BIOLOGICAL DEVELOPMENT AND FUNCTIONING;1.5 RESOURCES AND INFRASTRUCTURE (UNDERPINNING)
Health categories: GENERIC HEALTH RELEVANCE
Title: Using Knowledge Graph Learning to Predict and Explain Patient Outcomes in Electronic Health Records
Funder: Medical Research Council

Abstract: This fellowship will develop predictive analytics algorithms for knowledge graphs, and apply those methods to a biomedical graph to predict patient outcomes in electronic health records. The first steps are to develop the knowledge graph and optimise the predictive algorithms, building on my recent work [Bean et al. 2017]. The expanded graph will additionally include proteins, genes, ontologies, diseases and guidelines. Two predictive algorithms will be developed and optimised. A) Improving the performance of my published KG edge prediction method, focused on efficiency (gradient descent) and accuracy through to the use of both additional features (public knowledge and network features) and improved weighting of those features (confidence, provenance). B) New methods to predict the mechanistic basis for an edge in the graph. For example, the basis for a drug indication would involve the protein target of the drug, the pathways those proteins are involved in, and how those pathways relate to the indicated condition. Performance will be evaluated using the known mechanism of action for drugs. Both of these predictive algorithms will be validated using anonymised EHR data. Firstly, new predictions for outcomes will be generated from the graph and validated using anonymised EHR data extracted using CogStack (data surfacing and harmonisation) and the SemEHR (semantic annotation and search) projects at KCH. Secondly, one or more cohorts of patients with differential outcomes will be identified - for example treatment response or an unexpected drug-drug interaction. For these patients, classifiers will be trained to predict outcome from routine EHR data. The predictive features are essentially a set of clinical variables that are risk factors for the outcome. The explanation prediction algorithm will use these relationships to propose a mechanistic basis from the knowledge graph. Proposed mechanisms will be manually reviewed for plausibility.


Research activity codes: 1.4 METHODOLOGIES AND MEASUREMENTS;8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE
Title: Using semantics to leverage health and research Big Data
Funder: Medical Research Council

Abstract: The key aim of this research is to develop new methods for connecting clinical and non-clinical research big data. I will focus on harmonisation of two domains: "phenotype" due to its complexity, and "consent" due to its importance when making existing clinical data available for new purposes. The principles from this work will be applied to other domains, such as demographic and environmental data, to connect heterogeneous data of many dimensions. To harmonise phenotype data I will bring together clinical (Read Codes/SNOMED CT and ICD 10) and research (HPO and MeSH) focussed ontologies, with recent developments in ontology mapping and text mining to code clinical research data with both clinical and research ontologies. This will provide the interface for connecting with NHS and non-clinical 'omics data. A range of state of the art text mining tools (e.g. MetaMap, Bio-LarK, NCBO Annotator, cTAKES) will be assessed. Suitable tools will be optimised for concept selection and computationally intensive processing of big data that will exploit scalable storage (e.g. NoSQL) and infrastructure provided by the Leicester HPC facility. The finding, gathering and harmonisation of disease-centric public sources of 'omics data from databases, websites and scientific literature, will extend techniques we have developed for harmonising GWAS data and apply them to other types of 'omics, such as EWAS. I will investigate the utility of cross-species harmonisation of human phenotype terms to closely associated mouse phenotypes to answer translational research questions. This will make use of publically available HPO to MP mappings. I will collaborate with the relevant mapping projects to leverage existing semantic matching methods with real-world big data.


Research activity codes: 1.4 METHODOLOGIES AND MEASUREMENTS
Health categories: GENERIC HEALTH RELEVANCE
Title: Predictive analytics of integrated genomic and clinical data using machine learning and complex statistical approaches
Funder: Medical Research Council

Abstract: Although the utility of complex statistical, machine and deep learning (ML and DL) approaches in the context of multi-dimensional data has been clearly demonstrated, these methods have not been widely utilised to improve novel drug discovery and clinical risk prediction. This proposal aims to harness the potential of large-scale integrated genetic and health data to spur innovation, and develop predictive algorithms to improve clinical decision making and patient health. Specifically, this will focus on the development and evaluation of ML and DL frameworks for GWAS, and clinical risk prediction using publicly available large-scale EHR and genomics biodata resources, including UK Biobank, Genomics England and INTERVAL studies. Transcriptomic and functional data will be integrated into these using predictive approaches, where this has not been directly measured. This will be implemented in three stages: 1) assessment of complex time-dependent statistical approaches for modelling of hazard; 2) optimisation and assessment of existing ML and DL approaches for modelling of clinical risk; 3) development of novel approaches, specifically using recurrent neural networks (RNNs) to incorporate temporality and missingness in clinical data, including time varying covariates to accurately model complex hazard functions; the objective of this project will be to develop approaches that appropriately leverage the rich longitudinal and time-dependent data on individuals shown by us and others to substantially improve clinical risk prediction. In addition to risk prediction, this proposal will also focus on improving our understanding of genetic aetiology of disease. In addition to standard GWAS approaches, hybrid ML and GWAS approaches for prioritisation of candidate genes, and genetic variants associated with disease will also be applied, potentially improving the power to identify novel associations, with important implications for prioritisation of therapeutic targets.


Research activity codes: 1.4 METHODOLOGIES AND MEASUREMENTS
Health categories: GENERIC HEALTH RELEVANCE
Title: Systematic characterisation of genetically influenced 'omics' phenotypes and disease modules within biological networks
Funder: Medical Research Council

Abstract: Recent studies have been successful in identifying the association of genetic variants and potentially causal genes with various proteins, metabolites and lipids and their influence on key biological pathways that are associated with diseases [1-6]. In addition, there is increasing evidence of genetic overlap between unrelated diseases and traits that point to a shared aetiology of diseases [7-8]. The proposed work will focus on constructing GGMs for the multi-omics data (proteins, metabolites and lipids) with edges representing the partial correlation between two phenotype measures conditioned for other variables within the model. Meta-data which include genetic associations with multi-omics phenotypes from Genome-Wide Association Studies (GWAS), the genetic association with diseases from public databases (PhenoScanner, SNiPA etc.) and biological pathways (KEGG) will be added to the network to allow the identification of molecular pathways and disease modules using supervised learning methods. This will be particularly useful as an automated tool to detect disease modules within a biological network and inform mendelian randomisation studies (MR) to understand the shared aetiology of diseases. These genetically influenced disease modules can be tested for association across Electronic Health Record (HER) phenotypes (eg. Diagnosis, presence or absence of multi-morbidity and drug responses). This agnostic approach will provide insights into the influence of perturbations within the omics network on medical phenome and identify key omics phenotypes and pathways that are shared by diseases thereby providing candidate targets for therapeutic intervention. References: 1. Shin, S.-Y. et al. Nat.Genet. (2014) 2. Long, T. et al. Nat.Genet. (2017) 3. Kettunen, J. et al. Nat.Comm. (2016) 4. Suhre, K. et al. Nat. Comm. (2017) 5. Suhre, K. et al. Nature (2011) 6. Draisma, H. H. M. et al. Nat. Comm. (2015) 7. Sarwar et.al. Lancet. (2012) 8. Ferreira et.al PLoS Gen. (2013)


Research activity codes: 1.4 METHODOLOGIES AND MEASUREMENTS
Health categories: GENERIC HEALTH RELEVANCE
Title: Facilitating Deep Learning with Domain-Specific Knowledge
Funder: Medical Research Council

Abstract: Although a good conceptual fit, current methods in deep learning do not perform particularly well on biological data. One reason for this is the paucity of samples: we simply do not have enough data to train a neural network of sufficient size or complexity to learn the intricacies of our input datasets. Although we have many fewer biological samples, we have a much richer understanding of the underlying structure of the observations, as we have extensive prior knowledge about the interactions between genes. I am planning to use this rich prior knowledge to enhance the efficiency of the training process in two ways: 1) Appropriate data embedding A common technique in ML is to transform the input data into a form that is easier for the neural network to train upon (embedding). For example, high-dimensional data could be reduced using PCA to a lower dimensional form before use, thus removing unnecessary noise before training. I propose to develop similar methods using the large body of expertise in transcriptomic data analysis in Leeds to define suitable embedding strategies for preprocessing the input datasets. 2) Biologically appropriate network topologies A standard neural network consists of several hidden layers of neurones connected (both inside and between layers) in a regular manner. I propose a similar approach to learning gene expression analysis in which we use the vast amount of prior knowledge available in online repositories (such as the Gene Ontology, KEGG and String) to build intermediate neural network layers that model the known biological pathways and interactions in the data. This way, the ML approach does not need to learn these already-known interactions.


Research activity codes: 1.5 RESOURCES AND INFRASTRUCTURE (UNDERPINNING)
Health categories: GENERIC HEALTH RELEVANCE
Title: Automated Clinical Epidemiology Studies (ACES) platform for complex epidemiology study designs and diverse databases
Funder: Medical Research Council

Abstract: Work Package(WP)1: Development and validation of Automated Clinical Epidemiology Studies (ACES) software architecture for Automated Infant and Mothers Studies (AIMS) and Automated Pharmaco-Epidemiology Studies (APES) We will develop algorithms to link mother and baby pairs utilising linked primary and secondary care data for AIMS and implement evidence based methodologies identified or developed in WP2 for APES. Functional and technical validation will be performed. WP2: Precision methodologies for pharmaco-epidemiological studies Three key biases in utilising routinely collected data (RCD) for pharmaco-epidemiological studies are: prescription by indication bias; immortality time bias; and not accounting for unobserved confounders. We will conduct systematic reviews to identify potential methodologies to reduce these biases and recommend where and in which circumstances these methodologies should be applied. Where there are gaps in evidence we will propose new methodologies to mitigate them. WP3: Extend and evaluate our current ACES architecture to databases with differing nomenclature and structure We aim to extend our ACES architecture to enable studies to be conducted in diverse countries that have databases with differing nomenclature and structures. We will do this by developing algorithms to normalise structure and then by applying Extract, Transform and Load (ETL) architecture to conduct studies seamlessly across multiple databases. WP4: Identify and manage ethical issues of ACES There are ethical challenges with ACES tools that need to be identified and systems put in place to mitigate them; e.g. multiple associations can be rapidly tested and a researcher could pursue only those with positive associations. We will identify such risks and potential solutions for them by literature review, semi-structured interviewing with data providers and through focus groups with researchers. We will then develop solutions with key stakeholders.


Research activity codes: 1.5 RESOURCES AND INFRASTRUCTURE (UNDERPINNING)
Health categories: GENERIC HEALTH RELEVANCE
Title: Dissecting the molecular aetiology of complex traits using high dimensional omic data
Funder: Medical Research Council

Abstract: 1. Tissue-specific Mendelian randomization - As an initial analysis, I will instrument genes using tissue-specific expression quantitative trait loci (eQTL) and assess putative associations using Mendelian randomization (MR) across 139 complex traits. However, as more tissue-centric expression data becomes available I will be able to assess this crucial aspect of molecular aetiology with greater confidence by instrumenting genes with multiple instruments. I also plan to expand upon the initial set of traits assessed using ~600 outcomes from the UK Biobank study as well as proteomic data. 2. Systematic two-step epigenetic MR - Building upon recent research I have led, I intend to construct a framework to assess whether genetic and environmental exposures influence complex traits via changes in DNA methylation levels. To do this I will systematically apply two-step epigenetic MR. 3. Multivariable 'omic MR - I will apply the principals of multivariable MR to assess the effect of multiple 'omic traits simultaneously on complex traits/disease. This work will also evaluate pharmaceutical targets which should be translatable for therapeutic purposes. 4. Application of novel methods to 'omic data - Future methodology in this field is likely to focus on integrative approaches, and therefore I intend to apply the most innovative and cutting-edge methods to multiple types of 'omic data, as well as iterate upon them. 5. Computational tools to disseminate the product of this fellowship - I will develop a suite of bioinformatics tools and resources to disseminate the findings and methods of this fellowship to a broad audience. Building upon experience in developing a target validation tool for industry partners, I plan to develop platforms which automate fundamental analyses in this field to large-scale data which should be of considerable value to the community and industry partners.


Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;5.1 PHARMACEUTICALS
Health categories: GENERIC HEALTH RELEVANCE
Title: Assessing the feasibility of using smartwatches for low burden, high temporal density capture of longitudinal health data.
Funder: Medical Research Council

Abstract: Technical Summary of Activities 1.Develop smartwatch-based micro Ecological Momentary Assessment (uEMA) application (months 1-6 & 13-18, objectives A,B,C). This application will support all feasibility testing in the project. In addition to various self-report measures it will capture metrics for assessment of feasibility, including completion, compliance, and disruption. 2.Test feasibility of smartwatch-based uEMA in ALSPAC (months 7-30, objective A). The uEMA application will measure the Alcohol Urges Questionnaire in 20 participants from the ALSPAC study 6 times a day over 3 months. Completion, compliance, and self-reported disruption will be assessed, and in-depth interviews will highlight positive and negative aspects of the system. Differences in these by gender and socioeconomic background will be examined. 3.Test feasibility of combining smartwatch-based uEMA with passive detection of behaviour (months 19-30, objective B). The uEMA application will record mood and cigarette craving in 20 smokers 6 times a day over 3 months. Participants will be recruited from the Bristol Tobacco & Alcohol Research Group participant database. On the same smartwatch, the StopWatch system for passive detection of cigarette smoking (developed by applicant) will record details of cigarettes smoked a day. Completion, compliance, and disruption will again be assessed, and interviews will highlight experiences of the combined system. 4.Explore feasibility of combining smartwatch-based uEMA with cognitive testing (months 30-36, objective C). Working with collaborators Cambridge Cognition, the technical and regulatory aspects of combining cognitive assessment methods with other forms of measurement on smartwatches will be explored. 5.Develop analytical approaches (months 1-36, objective D). Appropriate methods will be developed for analysing longitudinal health data captured using Smartwatches, calling on time series analysis and multilevel modeling.


Research activity codes: 1.5 RESOURCES AND INFRASTRUCTURE (UNDERPINNING)
Health categories: GENERIC HEALTH RELEVANCE
Title: Intelligent Healthcare Systems for Large-scale Populations
Funder: Medical Research Council

Abstract: The first stage focuses on proof-of-concept studies. After getting access to UK Biobank and NE 85+, the inital step is Data Preprocessing, i.e. integration of complex data, such as MRI, accelerometer, and electronic health records. My previous AI model for infant stroke detection can be employed to explore some preliminary experiments, e.g. associations between human activities and specific diseases. Following this, utilising a multi-modal learning framework, a comprehensive model will be proposed to study the correlations between different health data sources. For example, what MRI pattern can correspond to a disease and how would that relate to the accelerometer data for human activity. Furthermore, attribute learning techniques can provide health feedback, e.g. how to implement a habit that can significantly reduce the risk of a disease. Finally, the model will be upgraded into an inference model and link the predictions to attributes, in a manner that is intuitive to understand.To further improve the interpretations, high-level visualisation technology, e.g. tSNE, Confusion Matrix, will provide furhter guidance about rationales behind the AI model. During the second stage, proof-of-concept studies will continue to the end of the project. Meanwhile, I plan to implement stable models on apps and smart sensors to encourage more participants to take parts in data collection. For example, stable models can be packaged into apps that can utilise mobile phone-embedded accelerometers as an approximate measurement for data collection and large-scale cohort clinical study. The expected outcomes of the project will be a series of high-quality publications both in AI and health data science fields. In addition, continued grant proposals will be submitted for the continued study of large-scale health data collection using the delivered system.


Research activity codes: 1.5 RESOURCES AND INFRASTRUCTURE (UNDERPINNING)
Health categories: GENERIC HEALTH RELEVANCE
Title: Methods for the privacy preserving analysis of sensitive health data: text analysis and data visualisation
Funder: Medical Research Council

Abstract: Health Data Science extracts, integrates and interprets health/biomedical data at population, organisation and individual levels to support: front-line clinical care/public health; health care planning/evaluation; research for academia, industry and the health/social services. The evolution of data science in the health/social sciences has lagged behind the physical sciences - including earth and space sciences where I am familiar. In part this reflects the social and technical challenges associated with governing human data in a responsible manner. My fellowship addresses methods and software to facilitate well-governed access, analysis and exploitation of sensitive health/biomedical data, with a joint focus on the privacy protected analysis of textual data and on guarding against the disclosure risk associated with data visualisation, particularly in several dimensions. Building on my earlier work, including as a Farr Future Leader, my fellowship will exploit big data analytics, text mining and new technologies in virtual and/or augmented reality. Novel approaches to disclosure control will be implemented via DataSHIELD, an open source software for the distributed analysis of sensitive data - where individual-level data can be analysed, but not seen or abstracted by the analyst. Embedded disclosure controls (set by the data custodian and inaccessible to the analyst) mitigate against inferential (analysis-based) disclosure and can avoid costly, error-prone human scrutiny of results. The fellowship builds on work that I have personally led including three years as manager of the DataSHIELD development team. The new functionality to be developed will allow DataSHIELD to act as an automated disclosure-control layer between the user and either medical text or data underpinning sophisticated visual representation. Key applications will include personalised medicine, epidemiology and modern public health with data coming from one source or several.


Research activity codes: 1.5 RESOURCES AND INFRASTRUCTURE (UNDERPINNING)
Health categories: GENERIC HEALTH RELEVANCE