Mitmekesiste bioloogiliste andmete ühendamine ja analüüs

Väitekirja elektrooniline versioon ei sisalda publikatsiooneTänu tehnoloogiate arengule on bioloogiliste andmete maht viimastel aastatel mitmekordistunud. Need andmed katavad erinevaid bioloogia valdkondi. Piirdudes vaid ühe andmestikuga saab bioloogilisi protsesse või haigusi uurida vaid ühest aspektist korraga. Seetõttu on tekkinud üha suurem vajadus masinõppe meetodite järele, mis aitavad kombineerida eri valdkondade andmeid, et uurida bioloogilisi protsesse tervikuna. Lisaks on nõudlus usaldusväärsete haigusspetsiifiliste andmestike kogude järele, mis võimaldaks vastavaid analüüse efektiivsemalt läbi viia. Käesolev väitekiri kirjeldab, kuidas rakendada masinõppel põhinevaid integratsiooni meetodeid erinevate bioloogiliste küsimuste uurimiseks. Me näitame kuidas integreeritud andmetel põhinev analüüs võimaldab paremini aru saada bioloogilistes protsessidest kolmes valdkonnas: Alzheimeri tõbi, toksikoloogia ja immunoloogia. Alzheimeri tõbi on vanusega seotud neurodegeneratiivne haigus millel puudub efektiivne ravi. Väitekirjas näitame, kuidas integreerida erinevaid Alzheimeri tõve spetsiifilisi andmestikke, et moodustada heterogeenne graafil põhinev Alzheimeri spetsiifiline andmestik HENA. Seejärel demonstreerime süvaõppe meetodi, graafi konvolutsioonilise tehisnärvivõrgu, rakendamist HENA-le, et leida potentsiaalseid haigusega seotuid geene. Teiseks uurisime kroonilist immuunpõletikulist haigust psoriaasi. Selleks kombineerisime patsientide verest ja nahast pärinevad laboratoorsed mõõtmised kliinilise infoga ning integreerisime vastavad analüüside tulemused tuginedes valdkonnaspetsiifilistel teadmistel. Töö viimane osa keskendub toksilisuse testimise strateegiate edasiarendusele. Toksilisuse testimine on protsess, mille käigus hinnatakse, kas uuritavatel kemikaalidel esineb organismile kahjulikke toimeid. See on vajalik näiteks ravimite ohutuse hindamisel. Töös me tuvastasime sarnase toimemehhanismiga toksiliste ühendite rühmad. Lisaks arendasime klassifikatsiooni mudeli, mis võimaldab hinnata uute ühendite toksilisust.A fast advance in biotechnological innovation and decreasing production costs led to explosion of experimental data being produced in laboratories around the world. Individual experiments allow to understand biological processes, e.g. diseases, from different angles. However, in order to get a systematic view on disease it is necessary to combine these heterogeneous data. The large amounts of diverse data requires building machine learning models that can help, e.g. to identify which genes are related to disease. Additionally, there is a need to compose reliable integrated data sets that researchers could effectively work with. In this thesis we demonstrate how to combine and analyze different types of biological data in the example of three biological domains: Alzheimer’s disease, immunology, and toxicology. More specifically, we combine data sets related to Alzheimer’s disease into a novel heterogeneous network-based data set for Alzheimer’s disease (HENA). We then apply graph convolutional networks, state-of-the-art deep learning methods, to node classification task in HENA to find genes that are potentially associated with the disease. Combining patient’s data related to immune disease helps to uncover its pathological mechanisms and to find better treatments in the future. We analyse laboratory data from patients’ skin and blood samples by combining them with clinical information. Subsequently, we bring together the results of individual analyses using available domain knowledge to form a more systematic view on the disease pathogenesis. Toxicity testing is the process of defining harmful effects of the substances for the living organisms. One of its applications is safety assessment of drugs or other chemicals for a human organism. In this work we identify groups of toxicants that have similar mechanism of actions. Additionally, we develop a classification model that allows to assess toxic actions of unknown compounds.https://www.ester.ee/record=b523255

Bayesian Network Modeling and Inference of GWAS Catalog

Genome-wide association studies (GWASs) have received an increasing attention to understand genotype-phenotype relationships. The Bayesian network has been proposed as a powerful tool for modeling single-nucleotide polymorphism (SNP)-trait associations due to its advantage in addressing the high computational complex and high dimensional problems. Most current works learn the interactions among genotypes and phenotypes from the raw genotype data. However, due to the privacy issue, genotype information is sensitive and should be handled by complying with specific restrictions. In this work, we aim to build Bayesian networks from publicly released GWAS statistics to explicitly reveal the conditional dependency between SNPs and traits. First, we focus on building a Bayesian network for modeling the SNP-categorical trait relationships. We construct a three-layered Bayesian network explicitly revealing the conditional dependency between SNPs and categorical traits from GWAS statistics. We then formulate inference problems based on the dependency relationship captured in the Bayesian network. Empirical evaluations show the effectiveness of our methods. Second, we focus on modeling the SNP-quantitative trait relationships. Existing methods in the literature can only deal with categorical traits. We address this limitation by leveraging the Conditional Linear Gaussian (CLG) Bayesian network, which can handle a mixture of discrete and continuous variables. A two-layered CLG Bayesian network is built where the SNPs are represented as discrete variables in one layer and quantitative traits are represented as continuous variables in another layer. Efficient inference methods are then derived based on the constructed network. The experimental results demonstrate the effectiveness of our methods. Finally, we present STIP, a web-based SNP-trait inference platform capable of a variety of inference tasks, such as trait inference given SNP genotypes and genotype inference given traits. The current version of STIP provides three services which are SNP-trait inference, Top-k trait prediction and GWAS catalog exploration

Automated cyber and privacy risk management toolkit

Addressing cyber and privacy risks has never been more critical for organisations. While a number of risk assessment methodologies and software tools are available, it is most often the case that one must, at least, integrate them into a holistic approach that combines several appropriate risk sources as input to risk mitigation tools. In addition, cyber risk assessment primarily investigates cyber risks as the consequence of vulnerabilities and threats that threaten assets of the investigated infrastructure. In fact, cyber risk assessment is decoupled from privacy impact assessment, which aims to detect privacy-specific threats and assess the degree of compliance with data protection legislation. Furthermore, a Privacy Impact Assessment (PIA) is conducted in a proactive manner during the design phase of a system, combining processing activities and their inter-dependencies with assets, vulnerabilities, real-time threats and Personally Identifiable Information (PII) that may occur during the dynamic life-cycle of systems. In this paper, we propose a cyber and privacy risk management toolkit, called AMBIENT (AutoMated cyBer and prIvacy risk managEmeNt Toolkit) that addresses the above challenges by implementing and integrating three distinct software tools. AMBIENT not only assesses cyber and privacy risks in a thorough and automated manner but it also offers decision-support capabilities, to recommend optimal safeguards using the well-known repository of the Center for Internet Security (CIS) Controls. To the best of our knowledge, AMBIENT is the first toolkit, in the academic literature, that brings together the aforementioned capabilities. To demonstrate its use, we have created a case scenario based on information about cyber attacks we have received from a healthcare organisation, as a reference sector that faces critical cyber and privacy threats

Systems Analytics and Integration of Big Omics Data

A “genotype"" is essentially an organism's full hereditary information which is obtained from its parents. A ""phenotype"" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome

Machine learning for large and small data biomedical discovery

In modern biomedicine, the role of computation becomes more crucial in light of the ever-increasing growth of biological data, which requires effective computational methods to integrate them in a meaningful way and unveil previously undiscovered biological insights. In this dissertation, we introduce a series of machine learning algorithms for biomedical discovery. Focused on protein functions in the context of system biology, these machine learning algorithms learn representations of protein sequences, structures, and networks in both the small- and large-data scenarios. First, we present a deep learning model that learns evolutionary contexts integrated representations of protein sequence and assists to discover protein variants with enhanced functions in protein engineering. Second, we describe a geometric deep learning model that learns representations of protein and compound structures to inform the prediction of protein-compound binding affinity. Third, we introduce a machine learning algorithm to integrate heterogeneous networks by learning compact network representations and to achieve drug repurposing by predicting novel drug-target interaction. We also present new scientific discoveries enabled by these machine learning algorithms. Taken together, this dissertation demonstrates the potential of machine learning to address the small- and large-data challenges of biomedical data and transform data into actionable insights and new discoveries

A deep generative model framework for creating high quality synthetic transaction sequences

Synthetic data are artificially generated data that closely model real-world measurements, and can be a valuable substitute for real data in domains where it is costly to obtain real data, or privacy concerns exist. Synthetic data has traditionally been generated using computational simulations, but deep generative models (DGMs) are increasingly used to generate high-quality synthetic data. In this thesis, we create a framework which employs DGMs for generating highquality synthetic transaction sequences. Transaction sequences, such as we may see in an online banking platform, or credit card statement, are important type of financial data for gaining insight into financial systems. However, research involving this type of data is typically limited to large financial institutions, as privacy concerns often prevent academic researchers from accessing this kind of data. Our work represents a step towards creating shareable synthetic transaction sequence datasets, containing data not connected to any actual humans. To achieve this goal, we begin by developing Banksformer, a DGM based on the transformer architecture, which is able to generate high-quality synthetic transaction sequences. Throughout the remainder of the thesis, we develop extensions to Banksformer that further improve the quality of data we generate. Additionally, we perform extensively examination of the quality synthetic data produced by our method, both with qualitative visualizations and quantitative metrics

Evolutionary computing driven search based software testing and correction

For a given program, testing, locating the errors identified, and correcting those errors is a critical, yet expensive process. The field of Search Based Software Engineering (SBSE) addresses these phases by formulating them as search problems. This dissertation addresses these challenging problems through the use of two complimentary evolutionary computing based systems. The first one is the Fitness Guided Fault Localization (FGFL) system, which novelly uses a specification based fitness function to perform fault localization. The second is the Coevolutionary Automated Software Correction (CASC) system, which employs a variety of evolutionary computing techniques to perform testing, correction, and verification of software. In support of the real world application of these systems, a practitioner\u27s guide to fitness function design is provided. For the FGFL system, experimental results are presented that demonstrate the applicability of fitness guided fault localization to automate this important phase of software correction in general, and the potential of the FGFL system in particular. For the fitness function design guide, the performance of a guide generated fitness function is compared to that of an expert designed fitness function demonstrating the competitiveness of the guide generated fitness function. For the CASC system, results are presented that demonstrate the system\u27s abilities on a series of problems of both increasing size as well as number of bugs present. The system presented solutions more than 90% of the time for versions of the programs containing one or two bugs. Additionally, scalability results are presented for the CASC system that indicate that success rate linearly decreases with problem size and that the estimated convergence rate scales at worst linearly with problem size --Abstract, page ii

Predicting and analyzing HIV-1 adaptation to broadly neutralizing antibodies and the host immune system using machine learning

Thanks to its extraordinarily high mutation and replication rate, the human immunodeficiency virus type 1 (HIV-1) is able to rapidly adapt to the selection pressure imposed by the host immune system or antiretroviral drug exposure. With neither a cure nor a vaccine at hand, viral control is a major pillar in the combat of the HIV-1 pandemic. Without drug exposure, interindividual differences in viral control are partly influenced by host genetic factors like the human leukocyte antigen (HLA) system, and viral genetic factors like the predominant coreceptor usage of the virus. Thus, a close monitoring of the viral population within the patients and adjustments in the treatment regimens, as well as a continuous development of new drug components are indispensable measures to counteract the emergence of viral escape variants. To this end, a fast and accurate determination of the viral adaptation is essential for a successful treatment. This thesis is based upon four studies that aim to develop and apply statistical learning methods to (i) predict adaptation of the virus to broadly neutralizing antibodies (bNAbs), a promising new treatment option, (ii) advance antibody-mediated immunotherapy for clinical usage, and (iii) predict viral adaptation to the HLA system to further understand the switch in HIV-1 coreceptor usage. In total, this thesis comprises several statistical learning approaches to predict HIV-1 adaptation, thereby, enabling a better control of HIV-1 infections.Dank seiner außergewöhnlich hohen Mutations- und Replikationsrate ist das humane Immundefizienzvirus Typ 1 (HIV-1) in der Lage sich schnell an den vom Immunsystem des Wirtes oder durch die antiretrovirale Arzneimittelexposition ausgeübten Selektionsdruck anzupassen. Da weder ein Heilmittel noch ein Impfstoff verfügbar sind, ist die Viruskontrolle eine wichtige Säule im Kampf gegen die HIV-1-Pandemie. Ohne Arzneimittelexposition werden interindividuelle Unterschiede in der Viruskontrolle teilweise durch genetische Faktoren des Wirts wie das humane Leukozytenantigensystem (HLA) und virale genetische Faktoren wie die vorherrschende Korezeptornutzung des Virus beeinflusst. Eine genaue Überwachung der Viruspopulation innerhalb des Patienten, gegebenfalls Anpassungen der Behandlungsschemata sowie eine kontinuierliche Entwicklung neuer Wirkstoffkomponenten sind daher unerlässliche Maßnahmen, um dem Auftreten viraler Fluchtvarianten entgegenzuwirken. Für eine erfolgreiche Behandlung ist eine schnelle und genaue Bestimmung der Anpassung einer Variante essentiell. Die Thesis basiert auf vier Studien, deren Ziel es ist statistische Lernverfahren zu entwickeln und anzuwenden, um (1) die Anpassung von HIV-1 an breit neutralisierende Antikörper, eine neuartige vielversprechende Therapieoption, vorherzusagen, (2) den Einsatz von Antikörper-basierte Immuntherapien für den klinischen Einsatz voranzutreiben, und (3) die virale Anpassung von HIV-1 an das HLA-System vorherzusagen, um den Wechsel der HIV-1 Korezeptornutzung besser zu verstehen. Zusammenfassend umfasst diese Thesis mehrere statistische Lernverfahrenansätze, um HIV Anpassung vorherzusagen, wodurch eine bessere Kontrolle von HIV-1 Infektionen ermöglicht wird

Secure, privacy-preserving and practical collaborative Genome-Wide Association Studies

Understanding the interplay between genomics and human health is a crucial step for the advancement and development of our society. Genome-Wide Association Study (GWAS) is one of the most popular methods for discovering correlations between genomic variations associated with a particular phenotype (i.e., an observable trait such as a disease). Leveraging genome data from multiple institutions worldwide nowadays is essential to produce more powerful findings by operating GWAS at larger scale. However, this raises several security and privacy risks, not only in the computation of such statistics, but also in the public release of GWAS results. To that extent, several solutions in the literature have adopted cryptographic approaches to allow secure and privacy-preserving processing of genome data for federated analysis. However, conducting federated GWAS in a secure and privacy-preserving manner is not enough since the public releases of GWAS results might be vulnerable to known genomic privacy attacks, such as recovery and membership attacks. The present thesis explores possible solutions to enable end-to-end privacy-preserving federated GWAS in line with data privacy regulations such as GDPR to secure the public release of the results of Genome-Wide Association Studies (GWASes) that are dynamically updated as new genomes become available, that might overlap with their genomes and considered locations within the genome, that can support internal threats such as colluding members in the federation and that are computed in a distributed manner without shipping actual genome data. While achieving these goals, this work created several contributions described below. First, the thesis proposes DyPS, a Trusted Execution Environment (TEE)-based framework that reconciles efficient and secure genome data outsourcing with privacy-preserving data processing inside TEE enclaves to assess and create private releases of dynamic GWAS. In particular, DyPS presents the conditions for the creation of safe dynamic releases certifying that the theoretical complexity of the solution space an external probabilistic polynomial-time (p.p.t.) adversary or a group of colluders (up to all-but-one parties) would need to infer when launching recovery attacks on the observation of GWAS statistics is large enough. Besides that, DyPS executes an exhaustive verification algorithm along with a Likelihood-ratio test to measure the probability of identifying individuals in studies. Thus, also protecting individuals against membership inference attacks. Only safe genome data (i.e., genomes and SNPs) that DyPS selects are further used for the computation and release of GWAS results. At the same time, the remaining (unsafe) data is kept secluded and protected inside the enclave until it eventually can be used. Our results show that if dynamic releases are not improperly evaluated, up to 8% of genomes could be exposed to genomic privacy attacks. Moreover, the experiments show that DyPS’ TEE-based architecture can accommodate the computational resources demanded by our algorithms and present practical running times for larger-scale GWAS. Secondly, the thesis offers I-GWAS that identifies the new conditions for safe releases when considering the existence of overlapping data among multiple GWASes (e.g., same individuals participating in several studies). Indeed, it is shown that adversaries might leverage information of overlapping data to make both recovery and membership attacks feasible again (even if they are produced following the conditions for safe single-GWAS releases). Our experiments show that up to 28.6% of genetic variants of participants could be inferred during recovery attacks, and 92.3% of these variants would enable membership attacks from adversaries observing overlapping studies, which are withheld by I-GWAS. Lastly yet importantly, the thesis presents GenDPR, which encompasses extensions to our protocols so that the privacy-verification algorithms can be conducted distributively among the federation members without demanding the outsourcing of genome data across boundaries. Further, GenDPR can also cope with collusion among participants while selecting genome data that can be used to create safe releases. Additionally, GenDPRproduces the same privacy guarantees as centralized architectures, i.e., it correctly identifies and selects the same data in need of protection as with centralized approaches. In the end, the thesis presents a homogenized framework comprising DyPS, I-GWAS and GenDPR simultaneously. Thus, offering a usable approach for conducting practical GWAS. The method chosen for protection is of a statistical nature, ensuring that the theoretical complexity of attacks remains high and withholding releases of statistics that would impose membership inference risks to participants using Likelihood-ratio tests, despite adversaries gaining additional information over time, but the thesis also relates the findings to techniques that can be leveraged to protect releases (such as Differential Privacy). The proposed solutions leverage Intel SGX as Trusted Execution Environment to perform selected critical operations in a performant manner, however, the work translates equally well to other trusted execution environments and other schemes, such as Homomorphic Encryption