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Found 4 papers, 0 papers with code
Handling highly correlated genes in prediction analysis of genomic studies
Method: We propose a grouping algorithm, which treats highly correlated genes as a group and uses their common pattern to represent the group's biological signal in feature selection.
Simultaneous prediction of multiple outcomes using revised stacking algorithms
Our goal is to build a model using data in this database, which simultaneously predicts the resistance of multiple drugs using mutation information from sequences of viruses for any new patient.
A Bayesian Group Sparse Multi-Task Regression Model for Imaging Genetics
Results: We develop a Bayesian hierarchical modeling formulation where the posterior mode corresponds to the estimator proposed by Wang et al. (Bioinformatics, 2012), and an approach that allows for full posterior inference including the construction of interval estimates for the regression parameters.
Regularization Parameter Selection for a Bayesian Multi-Level Group Lasso Regression Model with Application to Imaging Genomics
We demonstrate these problems through simulation examples and study an approximation to the marginal likelihood which sheds light on the cause of this problem.
