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Found 10 papers, 3 papers with code
Causal inference in drug discovery and development
To discover new drugs is to seek and to prove causality.
rfPhen2Gen: A machine learning based association study of brain imaging phenotypes to genotypes
Moreover, random forests identified additional SNPs that were not prioritized by the linear models but are known to be associated with brain-related disorders.
High-dimensional multi-trait GWAS by reverse prediction of genotypes
Multi-trait genome-wide association studies (GWAS) use multi-variate statistical methods to identify associations between genetic variants and multiple correlated traits simultaneously, and have higher statistical power than independent univariate analyses of traits.
Integrating Sensing and Communication in Cellular Networks via NR Sidelink
Since electromagnetic signals, through cellular communication systems, are omnipresent, RF sensing has the potential to become a universal sensing mechanism with applications in smart home, retail, localization, gesture recognition, intrusion detection, etc.
Tesla-Rapture: A Lightweight Gesture Recognition System from mmWave Radar Point Clouds
To tackle this issue, we developed Tesla, a Message Passing Neural Network (MPNN) graph convolution approach for mmWave radar point clouds.
Comparison between instrumental variable and mediation-based methods for reconstructing causal gene networks in yeast
Causal gene networks model the flow of information within a cell, but reconstructing them from omics data is challenging because correlation does not imply causation.
A Graph Feature Auto-Encoder for the Prediction of Unobserved Node Features on Biological Networks
Integrating the complementary viewpoints of biological networks and omics data is an important task in bioinformatics, but existing methods treat networks as discrete structures, which are intrinsically difficult to integrate with continuous node features or activity measures.
Restricted maximum-likelihood method for learning latent variance components in gene expression data with known and unknown confounders
Based on this result we propose a restricted maximum-likelihood method which estimates the latent variables by maximizing the likelihood on the restricted subspace orthogonal to the known confounding factors, and show that this reduces to probabilistic PCA on that subspace.
Wisdom of the crowd from unsupervised dimension reduction
Wisdom of the crowd, the collective intelligence derived from responses of multiple human or machine individuals to the same questions, can be more accurate than each individual, and improve social decision-making and prediction accuracy.
Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net
The lasso and elastic net linear regression models impose a double-exponential prior distribution on the model parameters to achieve regression shrinkage and variable selection, allowing the inference of robust models from large data sets.
