Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.

Formal development of resilient distributed systems

Resilience is the property of a system to remain trustworthy despite changes. Changes of a different nature, whether due to failures of system components or varying operational conditions, significantly increase the complexity of system development. Therefore, advanced development technologies are required to build robust and flexible system architectures capable of adapting to such changes. Moreover, powerful quantitative techniques are needed to assess the impact of these changes on various system characteristics. Architectural flexibility is achieved by embedding into the system design the mechanisms for identifying changes and reacting on them. Hence a resilient system should have both advanced monitoring and error detection capabilities to recognise changes as well as sophisticated reconfiguration mechanisms to adapt to them. The aim of such reconfiguration is to ensure that the system stays operational, i.e., remains capable of achieving its goals. Design, verification and assessment of the system reconfiguration mechanisms is a challenging and error prone engineering task. In this thesis, we propose and validate a formal framework for development and assessment of resilient systems. Such a framework provides us with the means to specify and verify complex component interactions, model their cooperative behaviour in achieving system goals, and analyse the chosen reconfiguration strategies. Due to the variety of properties to be analysed, such a framework should have an integrated nature. To ensure the system functional correctness, it should rely on formal modelling and verification, while, to assess the impact of changes on such properties as performance and reliability, it should be combined with quantitative analysis. To ensure scalability of the proposed framework, we choose Event-B as the basis for reasoning about functional correctness. Event-B is a statebased formal approach that promotes the correct-by-construction development paradigm and formal verification by theorem proving. Event-B has a mature industrial-strength tool support { the Rodin platform. Proof-based verification as well as the reliance on abstraction and decomposition adopted in Event-B provides the designers with a powerful support for the development of complex systems. Moreover, the top-down system development by refinement allows the developers to explicitly express and verify critical system-level properties. Besides ensuring functional correctness, to achieve resilience we also need to analyse a number of non-functional characteristics, such as reliability and performance. Therefore, in this thesis we also demonstrate how formal development in Event-B can be combined with quantitative analysis. Namely, we experiment with integration of such techniques as probabilistic model checking in PRISM and discrete-event simulation in SimPy with formal development in Event-B. Such an integration allows us to assess how changes and di erent recon guration strategies a ect the overall system resilience. The approach proposed in this thesis is validated by a number of case studies from such areas as robotics, space, healthcare and cloud domain.

Microparticles obtained by complex coacervation: influence of the type of reticulation and the drying process on the release of the core material

Microparticles obtained by complex coacervation were crosslinked with glutaraldehyde or with transglutaminase and dried using freeze drying or spray drying. Moist samples presented Encapsulation Efficiency (%EE) higher than 96%. The mean diameters ranged from 43.7 ± 3.4 to 96.4 ± 10.3 µm for moist samples, from 38.1 ± 5.36 to 65.2 ± 16.1 µm for dried samples, and from 62.5 ± 7.5 to 106.9 ± 26.1 µm for rehydrated microparticles. The integrity of the particles without crosslinking was maintained when freeze drying was used. After spray drying, only crosslinked samples were able to maintain the wall integrity. Microparticles had a round shape and in the case of dried samples rugged walls apparently without cracks were observed. Core distribution inside the particles was multinuclear and homogeneous and core release was evaluated using anhydrous ethanol. Moist particles crosslinked with glutaraldehyde at the concentration of 1.0 mM.g-1 protein (ptn), were more efficient with respect to the core retention compared to 0.1 mM.g-1 ptn or those crosslinked with transglutaminase (10 U.g-1 ptn). The drying processes had a strong influence on the core release profile reducing the amount released to all dry samples

Science Complex Opening

Pictured here from left to right are James Gibson, President Emeritus, R. A. Macleod, Board of Trustees, and Dr. Cecil Shaver, former Chancellor, during the 1984 Science Complex opening - an addition to the Mackenzie Chown Complex now simply known as H Block.

Science Complex Opening

Pictured here from left to right are Henry Tomarin, Board of Trustees, St. Catharines Mayor Roy Adams, R. Campbell, Niagara Region chairman, Peter Misener, and R. Misener, Chancellor, during the 1984 Science Complex opening - an addition to the Mackenzie Chown Complex now simply known as H Block.

Science Complex Opening

Dr. Earp unveils a sign at the joint Science Complex opening ceremony and the Academic Staging Building renaming ceremony. The Academic Staging Building was henceforth called the Mackenzie Chown Complex.

Science Complex Opening

Dr. Alan Earp speaks at the opening ceremony for the Science Complex addition in 1984.

Science Complex Opening

Doris Chown speaking at the Science Complex Opening and the unveiling of a sign in conjunction with the renaming of the Academic Staging Building to the Mackenzie Chown Complex.

Science Complex Opening

Robert S. K. Welch celebrates the opening of the new Science Complex wing, an addition to the Mackenzie Chown Complex, as Dr. Alan Earp (pictured behind Welch) and others look on. The new name for the Academic Staging Building was also unveiled. It was renamed after former mayor Mackenzie Chown.

Science Complex Opening

Rather then cutting a piece of red tape with scissors, the Science students at Brock prepared a laser devise to cut through a specially made piece of metallic ribbon for the opening ceremony of the Science Complex addition. Pictured here is Robert Welch with the laser device as he attempts to 'cut' the tape. Unfortunately the device failed and Dr. Earp resorted to cutting the tape with a Swiss Army knife he had on hand.

Greenhouse and Science Complex

The Aumni Greenhouse and the Science Complex in the background.

Complex Model

Mackenzie Chown Complex model.

Complex Model

View of the Mackenzie Chown Complex model from above.

Science Complex Model

A model showing the Mackenzie Chown complex and the H Block addition.