Automatic Inference of Graph Models for Complex Networks with Genetic Programming

Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.

The Relationship between Dementia Family Caregivers' Traditional Values and Beliefs about Caregiving and Well-being

Introduction: Canada’s aging population is diverse and this diversity will continue to grow for the next two decades (Government of Canada, 2002; Katz, 2005; Statistics Canada, 2010). Objective: to examine the relationship between dementia family caregivers’ traditionally-based beliefs about caregiving, their caregiving experience, and their well-being. Method: exploratory secondary data analysis of cross-sectional survey data from 76 community caregivers of persons with dementia in Ontario. Results: traditional values for caregiving was independently associated with coping resources and health status but not depression symptoms. Caregiver self-efficacy and social support both partially mediated the relationship between beliefs about caregiving and caregiver health status. Discussion: Findings from this exploratory study are consistent with stress process models of culture and caregiving. The finding that self-efficacy was associated with traditional values and that it mediated the relationship between traditional values and caregiver well-being is new to the literature.

Factors Associated with Small Aggressive Non-Small Cell Lung Carcinomas

Small aggressive non-small cell lung carcinomas (SA-NSCLC) are characterized by spread to distant lymph nodes and metastases, even while the primary tumour remains small in size, as opposed to tumours that are relatively large before cancer progression. These small aggressive cancers present a challenge for clinical diagnosis and screening, carry grave prognosis, and may benefit from using a targeted approach to identify high-risk individuals. The objectives of this thesis were to identify factors associated with SA-NSCLC, and compare survivorship of stage IV SA-NSCLC to large stage IV NSCLC. Logistic and Cox regression analysis were performed using data from the National Lung Screening Trial (NLST). Model building was guided by knowledge of lung carcinogenesis and lung cancer prognostic factors. Previous diagnosis of emphysema and positive family history of lung cancer in females were associated with increased risk of SA-NSCLC among adenocarcinomas. Despite overall poor prognosis, SA-NSCLC have a better prognosis compared to large stage IV NSCLC.