First, a regression-based approach40 identified neurogenic bHLH TFs such as NEUROG2, T-box TFs such as EOMES, and the RGC repressor HES1 (ref. 41) as correlated with increased reporter activity in IPCs (Extended
Single-cell and single-nucleus RNA-sequencing (sxRNA-seq) is increasingly being used to characterise the transcriptomic state of cell types at homeostasis, during development and in disease. However, this is a challenging task, as biological effects can
This section provides the most salient details of the proposed generalised joint regression modelling framework for count data. In particular, to account for competitive settings, we will focus on the methodological aspects that are relevant to the model specification adopted in Sect.4. Note that we...
(x, 2, params, gamma = 1e-4, sparsenessQuantile = 0.5, iterations = 10, smoothingMatrix = list(NA, NA), verbose = TRUE ) # latent feature mask <- getMask(antsImageRead(getANTsRData("r16"))) spatmat <- t(imageDomainToSpatialMatrix(mask, mask)) smoomat <- knnSmoo...
Comparatively, 5% quantile value is the best expected value, and the score is the difference between the maximum value and the measured value divided by the difference between the maximum value and the best expected value for the negative correlation indexes. If the score is greater than 1, it...
We used Poisson regression to model the relative risk of primary THR per unit time stratified by gender across SES quintiles, and adjusted for age (as a categorical variable). Given the interaction between SES and gender, the model was set up as: log(N) = log(PAR) + intercept...
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For the in-house fpVCT data, we first clipped at the0.01-th and0.99-th quantile value and subsequently rescaled voxel intensity values such that a meanμμ=0and unit standard deviationσ=1normal distributionN(0,1)emerges for the full datasets. The parameter values for quantileq, meanμμand...
Short-term load probabilistic forecasting based on quantile regression convolutional neural network and Epanechnikov kernel density estimation. Energy Rep. 2020, 6, 1550–1556. [Google Scholar] [CrossRef] Han, J.; Pei, J.; Yin, Y.; Mao, R. Mining frequent patterns without candidate generation:...