ATHENA: A knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci

<p>Abstract</p> <p>Background</p> <p>Growing interest and burgeoning technology for discovering genetic mechanisms that influence disease processes have ushered in a flood of genetic association studies over the last decade, yet little heritability in highly studied complex traits has been explained by genetic variation. Non-additive gene-gene interactions, which are not often explored, are thought to be one source of this "missing" heritability.</p> <p>Methods</p> <p>Stochastic methods employing evolutionary algorithms have demonstrated promise in being able to detect and model gene-gene and gene-environment interactions that influence human traits. Here we demonstrate modifications to a neural network algorithm in ATHENA (the Analysis Tool for Heritable and Environmental Network Associations) resulting in clear performance improvements for discovering gene-gene interactions that influence human traits. We employed an alternative tree-based crossover, backpropagation for locally fitting neural network weights, and incorporation of domain knowledge obtainable from publicly accessible biological databases for initializing the search for gene-gene interactions. We tested these modifications <it>in silico </it>using simulated datasets.</p> <p>Results</p> <p>We show that the alternative tree-based crossover modification resulted in a modest increase in the sensitivity of the ATHENA algorithm for discovering gene-gene interactions. The performance increase was highly statistically significant when backpropagation was used to locally fit NN weights. We also demonstrate that using domain knowledge to initialize the search for gene-gene interactions results in a large performance increase, especially when the search space is larger than the search coverage.</p> <p>Conclusions</p> <p>We show that a hybrid optimization procedure, alternative crossover strategies, and incorporation of domain knowledge from publicly available biological databases can result in marked increases in sensitivity and performance of the ATHENA algorithm for detecting and modelling gene-gene interactions that influence a complex human trait.</p

Talking about space : the Spatial Reference System of Irish English

THESIS 10620This thesis has three central goals. The first is to establish a system for how people used to talk about space in previous studies of Irish English. This Is done through constructing a Spatial Reference System from extant traditional dialect studies of Irish English. Those dialect findings are then evaluated against the two primary views of Irish English today; substrate transfer from Irish and standardisation. Secondly, through interviews and questionnaire responses, I will determine how people now talk about space

Standard language ideology in an English-medium Irish secondary school: Conflicting perspectives on the discouragement of nonstandard language

The current paper aims to address how one English-medium school functions from the different perspectives within the school: the principal, student/teacher classroom interaction and the students. This approach allows us to see the power differential of the different stakeholders in a school and how iconisation, fractal recursivity, and erasure affect teenagers in Dublin. This paper presents interview data with a principal and the students in a secondary school. Taking a qualitative approach to these data, I show that standard language ideology is linked with economic disadvantage. The school principal’s approach to identifying, problematising and seeking to eliminate certain types of nonstandard language in the school reflects a standard language ideology and is consistent with a raciolinguistic approach to linguistic discrimination. The data suggest that the students themselves take a more nuanced approach