837 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
Enhancing Super-Resolution Networks through Realistic Thick-Slice CT Simulation
This study aims to develop and evaluate an innovative simulation algorithm
for generating thick-slice CT images that closely resemble actual images in the
AAPM-Mayo's 2016 Low Dose CT Grand Challenge dataset. The proposed method was
evaluated using Peak Signal-to-Noise Ratio (PSNR) and Root Mean Square Error
(RMSE) metrics, with the hypothesis that our simulation would produce images
more congruent with their real counterparts. Our proposed method demonstrated
substantial enhancements in terms of both PSNR and RMSE over other simulation
methods. The highest PSNR values were obtained with the proposed method,
yielding 49.7369 2.5223 and 48.5801 7.3271 for D45 and B30
reconstruction kernels, respectively. The proposed method also registered the
lowest RMSE with values of 0.0068 0.0020 and 0.0108 0.0099 for D45
and B30, respectively, indicating a distribution more closely aligned with the
authentic thick-slice image. Further validation of the proposed simulation
algorithm was conducted using the TCIA LDCT-and-Projection-data dataset. The
generated images were then leveraged to train four distinct super-resolution
(SR) models, which were subsequently evaluated using the real thick-slice
images from the 2016 Low Dose CT Grand Challenge dataset. When trained with
data produced by our novel algorithm, all four SR models exhibited enhanced
performance.Comment: 11 pages, 4 figure
- …