22 citations · 55 across the 9 of their papers we have counts for
9 papers
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…
Tutorial on survival modeling with applications to omics data
Zhi Zhao, John Zobolas, Manuela Zucknick +1
Motivation: Identification of genomic, molecular and clinical markers prognostic of patient survival is important for developing personalized disease prevention, diagnostic and tre…
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…
Composite local low-rank structure in learning drug sensitivity
The Tien Mai, Leiv Rønneberg, Zhi Zhao +2
The molecular characterization of tumor samples by multiple omics data sets of different types or modalities (e.g. gene expression, mutation, CpG methylation) has become an invalua…
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…