22 citations · 55 across the 9 of their papers we have counts for
7 papers · 1 filter
PliableBVS: A flexible Bayesian variable selection method for modeling interactions with mandatory modifying variables
Theophilus Quachie Asenso, Zhi Zhao, Maren-Helene Langeland Degnes +3
High-dimensional interaction models are useful for studying, for example, how a large set of variables of interest, such as gene expression or other omics features, interact with a…
Bayesian Cox model with graph-structured variable selection priors for multi-omics biomarker identification
Tobias Østmo Hermansen, Manuela Zucknick, Zhi Zhao
An important goal in cancer research is the survival prognosis of a patient based on a minimal panel of genomic and molecular markers such as genes or proteins. Purely data-driven…
A note on promotion time cure models with a new biological consideration
Zhi Zhao, Fatih Kızılaslan
We introduce a generalized promotion time cure model motivated by a new biological consideration. The new approach is flexible to model heterogeneous survival data, in particular f…
Comparison of the LASSO and Integrative LASSO with Penalty Factors (IPF-LASSO) methods for multi-omics data: Variable selection with Type I error control
Charlotte Castel, Zhi Zhao, Magne Thoresen
Variable selection in relation to regression modeling has constituted a methodological problem for more than 60 years. Especially in the context of high-dimensional regression, dev…
BayesSUR: An R package for high-dimensional multivariate Bayesian variable and covariance selection in linear regression
Zhi Zhao, Marco Banterle, Leonardo Bottolo +3
In molecular biology, advances in high-throughput technologies have made it possible to study complex multivariate phenotypes and their simultaneous associations with high-dimensio…
Multivariate Bayesian structured variable selection for pharmacogenomic studies
Zhi Zhao, Marco Banterle, Alex Lewin +1
Precision cancer medicine aims to determine the optimal treatment for each patient. In-vitro cancer drug sensitivity screens combined with multi-omics characterization of the cance…
Structured penalized regression for drug sensitivity prediction
Zhi Zhao, Manuela Zucknick
Large-scale {\it in vitro} drug sensitivity screens are an important tool in personalized oncology to predict the effectiveness of potential cancer drugs. The prediction of the sen…