950 research outputs found
BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies
Gene-gene interactions have long been recognized to be fundamentally
important to understand genetic causes of complex disease traits. At present,
identifying gene-gene interactions from genome-wide case-control studies is
computationally and methodologically challenging. In this paper, we introduce a
simple but powerful method, named `BOolean Operation based Screening and
Testing'(BOOST). To discover unknown gene-gene interactions that underlie
complex diseases, BOOST allows examining all pairwise interactions in
genome-wide case-control studies in a remarkably fast manner. We have carried
out interaction analyses on seven data sets from the Wellcome Trust Case
Control Consortium (WTCCC). Each analysis took less than 60 hours on a standard
3.0 GHz desktop with 4G memory running Windows XP system. The interaction
patterns identified from the type 1 diabetes data set display significant
difference from those identified from the rheumatoid arthritis data set, while
both data sets share a very similar hit region in the WTCCC report. BOOST has
also identified many undiscovered interactions between genes in the major
histocompatibility complex (MHC) region in the type 1 diabetes data set. In the
coming era of large-scale interaction mapping in genome-wide case-control
studies, our method can serve as a computationally and statistically useful
tool.Comment: Submitte
Probabilistic Forecasting of Photovoltaic Generation: An Efficient Statistical Approach
A novel efficient probabilistic forecasting approach is proposed to accurately quantify the variability and uncertainty of the power production from photovoltaic (PV) systems. Distinguished from most existing models, a linear programming based prediction interval construction model for PV power generation is constructed based on extreme learning machine and quantile regression, featuring high reliability and computational efficiency. The proposed approach is validated through the numerical studies on PV data from Denmark.Department of Electrical Engineerin
Probabilistic forecasting of wind power generation using extreme learning machine.
Accurate and reliable forecast of wind power is essential to power system operation and control. However, due to the nonstationarity of wind power series, traditional point forecasting can hardly be accurate, leading to increased uncertainties and risks for system operation. This paper proposes an extreme learning machine (ELM)-based probabilistic forecasting method for wind power generation. To account for the uncertainties in the forecasting results, several bootstrap methods have been compared for modeling the regression uncertainty, based on which the pairs bootstrap method is identified with the best performance. Consequently, a new method for prediction intervals formulation based on the ELM and the pairs bootstrap is developed. Wind power forecasting has been conducted in different seasons using the proposed approach with the historical wind power time series as the inputs alone. The results demonstrate that the proposed method is effective for probabilistic forecasting of wind power generation with a high potential for practical applications in power systems
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