Machine learning to model health with multimodal mobile sensor data

The widespread adoption of smartphones and wearables has led to the accumulation of rich datasets, which could aid the understanding of behavior and health in unprecedented detail. At the same time, machine learning and specifically deep learning have reached impressive performance in a variety of prediction tasks, but their use on time-series data appears challenging. Existing models struggle to learn from this unique type of data due to noise, sparsity, long-tailed distributions of behaviors, lack of labels, and multimodality. This dissertation addresses these challenges by developing new models that leverage multi-task learning for accurate forecasting, multimodal fusion for improved population subtyping, and self-supervision for learning generalized representations. We apply our proposed methods to challenging real-world tasks of predicting mental health and cardio-respiratory fitness through sensor data. First, we study the relationship of passive data as collected from smartphones (movement and background audio) to momentary mood levels. Our new training pipeline, which combines different sensor data into a low-dimensional embedding and clusters longitudinal user trajectories as outcome, outperforms traditional approaches based solely on psychology questionnaires. Second, motivated by mood instability as a predictor of poor mental health, we propose encoder-decoder models for time-series forecasting which exploit the bi-modality of mood with multi-task learning. Next, motivated by the success of general-purpose models in vision and language tasks, we propose a self-supervised neural network ready-to-use as a feature extractor for wearable data. To this end, we set the heart rate responses as the supervisory signal for activity data, leveraging their underlying physiological relationship and show that the resulting task-agnostic embeddings can generalize in predicting structurally different downstream outcomes through transfer learning (e.g. BMI, age, energy expenditure), outperforming unsupervised autoencoders and biomarkers. Finally, acknowledging fitness as a strong predictor of overall health, which, however, can only be measured with expensive instruments (e.g., a VO2max test), we develop models that enable accurate prediction of fine-grained fitness levels with wearables in the present, and more importantly, its direction and magnitude almost a decade later. All proposed methods are evaluated on large longitudinal datasets with tens of thousands of participants in the wild. The models developed and the insights drawn in this dissertation provide evidence for a better understanding of high-dimensional behavioral and physiological data with implications for large-scale health and lifestyle monitoring.The Department of Computer Science and Technology at the University of Cambridge through the EPSRC through Grant DTP (EP/N509620/1), and the Embiricos Trust Scholarship of Jesus College Cambridg

Machine learning techniques for identification using mobile and social media data

Networked access and mobile devices provide near constant data generation and collection. Users, environments, applications, each generate different types of data; from the voluntarily provided data posted in social networks to data collected by sensors on mobile devices, it is becoming trivial to access big data caches. Processing sufficiently large amounts of data results in inferences that can be characterized as privacy invasive. In order to address privacy risks we must understand the limits of the data exploring relationships between variables and how the user is reflected in them. In this dissertation we look at data collected from social networks and sensors to identify some aspect of the user or their surroundings. In particular, we find that from social media metadata we identify individual user accounts and from the magnetic field readings we identify both the (unique) cellphone device owned by the user and their course-grained location. In each project we collect real-world datasets and apply supervised learning techniques, particularly multi-class classification algorithms to test our hypotheses. We use both leave-one-out cross validation as well as k-fold cross validation to reduce any bias in the results. Throughout the dissertation we find that unprotected data reveals sensitive information about users. Each chapter also contains a discussion about possible obfuscation techniques or countermeasures and their effectiveness with regards to the conclusions we present. Overall our results show that deriving information about users is attainable and, with each of these results, users would have limited if any indication that any type of analysis was taking place

Computational methods for single cell RNA and genome assembly resolution using genetic variation

Genetic variation and natural selection have driven the evolutionary history on this planet and are responsible for creating us and all other life as we know it. Over the past several decades, the genomic revolution has allowed us to assess population variation across humans and other species and use that to link genotypes with phenotypes and infer evolutionary histories. In this thesis, I explore computational methods for using genetic variation to demultiplex and disambiguate complex data. In single cell RNAseq, problems of batch effects, doublets, and ambient RNA are each sources of noise that impede our ability to infer the functional states of cells and compare them between experiments. One new popular new experimental design promising to solve each of these while also reducing experimental costs is mixturing multiple individuals' cells into a single experiment. In chapter 2, I present a method for clustering cells by genotype, calling doublets, and using the cross-genotype signal in singletons to estimate and remove ambient RNA. I compare this methods to other existing methods including one that requires \textit{a priori} information about the genotypes, and two which do not. I find that my method outperforms each of these methods across a wide range of data parameters and sample types. In genome assembly, the recent higher throughput and lower cost of long read sequencing has revolutionized our ability to create reference quality genomes and has revitalized the assembly community. Now, massive efforts are taking place in the Darwin Tree of Life project and the Earth Biogenome project to create reference genomes for all multicelular eukaryotic life. This will create a scientific resource for the next generation of biological science, will serve as a conservation of data that could otherwise be lost in this time of mass extinction, and will allow for a much more broad understanding of evolution and the evolutionary history of life on Earth. While much progress has been made in data quality and assembly algorithms, some problems still exist. Until recently, the DNA input requirements for long read sequencing technologies made it impossible to sequence single individuals of these species with long reads. Also, high heterozygosity makes assembly more difficult due to the inherent ambiguity between heterozygous sequence versus paralogous sequence when confronted with inexact homology. One solution to the DNA input requirements would be to pool individuals, but this only increases the heterozygosity of the sample and reduces assembly quality. In chapter 3, we present the first high quality assembly of a single mosquito using new library preparation methods with reduced DNA requirements. This reduces the number of haplotypes to two, improving the assembly quality. In chapter 4, we further address the problems brought on by heterozygosity in assembly. I present a suite of tools that use the phasing consistency of multiple heterozygous sequences as a signal for physical linkage, thus using genetic variation to our advantage rather than as a challenge to overcome. This tool creates phased, linked assemblies and phasing aware scaffolding. Further, I provide a tool for phasing aware scaffolding on existing assemblies. This includes a novel haplotype phasing algorithm with some unique beneficial properties. It is robust to non-heterozygous variants as input and can detect and correct those genotypes. And it naturally extends to polyploid genomes.Wellcome Trus

Ranked Retrieval in Uncertain and Probabilistic Databases

Ranking queries are widely used in data exploration, data analysis and decision making scenarios. While most of the currently proposed ranking techniques focus on deterministic data, several emerging applications involve data that are imprecise or uncertain. Ranking uncertain data raises new challenges in query semantics and processing, making conventional methods inapplicable. Furthermore, the interplay between ranking and uncertainty models introduces new dimensions for ordering query results that do not exist in the traditional settings. This dissertation introduces new formulations and processing techniques for ranking queries on uncertain data. The formulations are based on marriage of traditional ranking semantics with possible worlds semantics under widely-adopted uncertainty models. In particular, we focus on studying the impact of tuple-level and attribute-level uncertainty on the semantics and processing techniques of ranking queries. Under the tuple-level uncertainty model, we introduce a processing framework leveraging the capabilities of relational database systems to recognize and handle data uncertainty in score-based ranking. The framework encapsulates a state space model, and efficient search algorithms that compute query answers by lazily materializing the necessary parts of the space. Under the attribute-level uncertainty model, we give a new probabilistic ranking model, based on partial orders, to encapsulate the space of possible rankings originating from uncertainty in attribute values. We present a set of efficient query evaluation algorithms, including sampling-based techniques based on the theory of Markov chains and Monte-Carlo method, to compute query answers. We build on our techniques for ranking under attribute-level uncertainty to support rank join queries on uncertain data. We show how to extend current rank join methods to handle uncertainty in scoring attributes. We provide a pipelined query operator implementation of uncertainty-aware rank join algorithm integrated with sampling techniques to compute query answers

Database Usability Enhancement in Data Exploration

Database usability has become an important research topic over the last decade. In the early days, database management systems were maintained by sophisticated users like database administrators. Today, due to the availability of data and computing resources, more non-expert users are involved in database computation. From their point of view, database systems lack ease of use. So researchers believe that usability is as important as the performance and functionality of databases and therefore developed many techniques such as natural language interface to enhance the ease of use of databases. In this thesis, we find some deeper technical issues in database usability, so we look at several core database technologies to further improve the ease of use of databases in two dimensions: we help users process data and exploit computing capacities. We start by helping users find the data. In the real world, public data is everywhere on the Web, but it is scattered around. We extract a prototype relational knowledge base to solve this problem. We start from the most basic binary mapping relationships (sometimes also named bridge tables) between entities from the web. This mapping relationship facilitates many data transformation applications such as auto-correct, auto-fill, and auto-join. After finding the data, we help users explore the data. When users issue queries to explore the data, their query results may contain too many items. So the system designer has to present a small subset of representative and diverse items rather than all items. This is known as the query result diversification problem. We propose the RC-Index, which helps to solve the diversification problem by significantly reducing the number of items that must be retrieved by the database to form a diverse set of a desired size. It is nearly an order of magnitude faster than the state-of-the-art and has a good performance guarantee, which improves the ease of use of databases in terms of querying. Finally, we shift our focus from data to computing capacities. We propose a framework to help users choose configurations in the cloud. Cloud computing has revolutionized data analysis, but choosing the right configuration is challenging because the common pricing mechanism of the public cloud is too complicated. Users have to consider low-level resources to find the best plan for their computational tasks. To address this issue, we propose a new market-based framework for pricing computational tasks in the cloud. We introduce agents to help users configure their personalized databases, which improves the ease of use of databases in the cloud

Exploiting general-purpose background knowledge for automated schema matching

The schema matching task is an integral part of the data integration process. It is usually the first step in integrating data. Schema matching is typically very complex and time-consuming. It is, therefore, to the largest part, carried out by humans. One reason for the low amount of automation is the fact that schemas are often defined with deep background knowledge that is not itself present within the schemas. Overcoming the problem of missing background knowledge is a core challenge in automating the data integration process. In this dissertation, the task of matching semantic models, so-called ontologies, with the help of external background knowledge is investigated in-depth in Part I. Throughout this thesis, the focus lies on large, general-purpose resources since domain-specific resources are rarely available for most domains. Besides new knowledge resources, this thesis also explores new strategies to exploit such resources. A technical base for the development and comparison of matching systems is presented in Part II. The framework introduced here allows for simple and modularized matcher development (with background knowledge sources) and for extensive evaluations of matching systems. One of the largest structured sources for general-purpose background knowledge are knowledge graphs which have grown significantly in size in recent years. However, exploiting such graphs is not trivial. In Part III, knowledge graph em- beddings are explored, analyzed, and compared. Multiple improvements to existing approaches are presented. In Part IV, numerous concrete matching systems which exploit general-purpose background knowledge are presented. Furthermore, exploitation strategies and resources are analyzed and compared. This dissertation closes with a perspective on real-world applications

Building high-quality merged ontologies from multiple sources with requirements customization

Ontologies are the prime way of organizing data in the Semantic Web. Often, it is necessary to combine several, independently developed ontologies to obtain a knowledge graph fully representing a domain of interest. Existing approaches scale rather poorly to the merging of multiple ontologies due to using a binary merge strategy. Thus, we aim to investigate the extent to which the n-ary strategy can solve the scalability problem. This thesis contributes to the following important aspects: 1. Our n-ary merge strategy takes as input a set of source ontologies and their mappings and generates a merged ontology. For efficient processing, rather than successively merging complete ontologies pairwise, we group related concepts across ontologies into partitions and merge first within and then across those partitions. 2. We take a step towards parameterizable merge methods. We have identified a set of Generic Merge Requirements (GMRs) that merged ontologies might be expected to meet. We have investigated and developed compatibilities of the GMRs by a graph-based method. 3. When multiple ontologies are merged, inconsistencies can occur due to different world views encoded in the source ontologies To this end, we propose a novel Subjective Logic-based method to handling the inconsistency occurring while merging ontologies. We apply this logic to rank and estimate the trustworthiness of conflicting axioms that cause inconsistencies within a merged ontology. 4. To assess the quality of the merged ontologies systematically, we provide a comprehensive set of criteria in an evaluation framework. The proposed criteria cover a variety of characteristics of each individual aspect of the merged ontology in structural, functional, and usability dimensions. 5. The final contribution of this research is the development of the CoMerger tool that implements all aforementioned aspects accessible via a unified interface

Exploring the importance of cell-type-specific gene expression regulation and splicing in Parkinson’s disease

Parkinson’s disease (PD) is defined primarily as a movement disorder, but its symptoms extend beyond the diagnosis-defining motor symptoms. Among non-motor symptoms, dementia is one of the most common and debilitating, yet it remains relatively understudied in comparison to motor symptoms, in part due to the considerable clinical, genetic and pathologic overlap between Parkinson’s disease with dementia (PDD) and dementia with Lewy bodies (DLB). Common to all three diseases is a lack of disease-modifying therapies, the development of which requires knowledge of the genes, cell types and biological pathways affected in disease. In this thesis, publicly available brain-relevant functional genomic annotations were used to identify PD-relevant pathways and cell types in silico. PD heritability was not found enriched in a specific cell type or state; however, PD heritability was found significantly enriched in a lysosomal and loss-of-function-intolerant gene set, with the former highly expressed in astrocytic, microglial, and oligodendrocyte subtypes and the latter highly expressed in almost all tested cellular subtypes. In addition, new annotations were generated by applying bulk-tissue and single-nucleus RNA-sequencing to anterior cingulate cortex samples derived from individuals with PD, PDD and DLB. This pairing permitted cellular deconvolution of bulk-tissue gene expression; estimation of bulk-tissue cell-type abundances; and in-depth splicing analyses. These analyses found that PD, PDD and DLB were associated not just with one, but several cell types, including neuronal, glial and vascular cell types, suggesting that these are disorders of global pathways working across various cell types. Furthermore, these analyses illustrated the commonalities and differences between the three diseases in terms of associated pathways, cell types, and upstream regulators of splicing, observations that can be used to begin building a biological basis on which to distinguish these disorders