Ood-gnn: Out-of-distribution generalized graph neural network
Graph neural networks (GNNs) have achieved impressive performance when testing and
training graph data come from identical distribution. However, existing GNNs lack out-of …
training graph data come from identical distribution. However, existing GNNs lack out-of …
Sequence determinants of human gene regulatory elements
DNA can determine where and when genes are expressed, but the full set of sequence
determinants that control gene expression is unknown. Here, we measured the …
determinants that control gene expression is unknown. Here, we measured the …
Detection of differentially abundant cell subpopulations in scRNA-seq data
Comprehensive and accurate comparisons of transcriptomic distributions of cells from
samples taken from two different biological states, such as healthy versus diseased …
samples taken from two different biological states, such as healthy versus diseased …
Detection of chronic kidney disease and selecting important predictive attributes
A Salekin, J Stankovic - 2016 IEEE International Conference on …, 2016 - ieeexplore.ieee.org
Chronic kidney disease (CKD) is a major public health concern with rising prevalence. In
this study we consider 24 predictive parameters and create a machine learning classifier to …
this study we consider 24 predictive parameters and create a machine learning classifier to …
Large-scale citizen science reveals predictors of sensorimotor adaptation
Sensorimotor adaptation is essential for keeping our movements well calibrated in response
to changes in the body and environment. For over a century, researchers have studied …
to changes in the body and environment. For over a century, researchers have studied …
On the prediction performance of the lasso
Although the Lasso has been extensively studied, the relationship between its prediction
performance and the correlations of the covariates is not fully understood. In this paper, we …
performance and the correlations of the covariates is not fully understood. In this paper, we …
Non-negative least squares for high-dimensional linear models: Consistency and sparse recovery without regularization
M Slawski, M Hein - 2013 - projecteuclid.org
Least squares fitting is in general not useful for high-dimensional linear models, in which the
number of predictors is of the same or even larger order of magnitude than the number of …
number of predictors is of the same or even larger order of magnitude than the number of …
Forecasting the bearing capacity of the driven piles using advanced machine-learning techniques
Estimating the bearing capacity of piles is an essential point when seeking for safe and
economic geotechnical structures. However, the traditional methods employed in this …
economic geotechnical structures. However, the traditional methods employed in this …
Genome-wide identification of lineage and locus specific variation associated with pneumococcal carriage duration
Streptococcus pneumoniae is a leading cause of invasive disease in infants, especially in
low-income settings. Asymptomatic carriage in the nasopharynx is a prerequisite for …
low-income settings. Asymptomatic carriage in the nasopharynx is a prerequisite for …
A tuning-free robust and efficient approach to high-dimensional regression
We introduce a novel approach for high-dimensional regression with theoretical guarantees.
The new procedure overcomes the challenge of tuning parameter selection of Lasso and …
The new procedure overcomes the challenge of tuning parameter selection of Lasso and …