22 citations · 62 across the 11 of their papers we have counts for
11 papers
Generalized promotion time cure model: A new modeling framework to identify cell-type-specific genes and improve survival prognosis
Zhi Zhao, Fatih Kızılaslan, Shixiong Wang +1
Single-cell technologies provide an unprecedented opportunity for dissecting the interplay between the cancer cells and the associated tumor microenvironment, and the produced high…
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…
An ADMM approach for multi-response regression with overlapping groups and interaction effects
Theophilus Quachie Asenso, Manuela Zucknick
In this paper, we consider the regularized multi-response regression problem where there exists some structural relation within the responses and also between the covariates and a…
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…
Combining heterogeneous subgroups with graph-structured variable selection priors for Cox regression
Katrin Madjar, Manuela Zucknick, Katja Ickstadt +1
Important objectives in cancer research are the prediction of a patient's risk based on molecular measurements such as gene expression data and the identification of new prognostic…
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…
A Bayesian approach to study synergistic interaction effects in in-vitro drug combination experiments
Andrea Cremaschi, Arnoldo Frigessi, Kjetil Taskén +1
In cancer translational research, increasing effort is devoted to the study of the combined effect of two drugs when they are administered simultaneously. In this paper, we introdu…
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…
MCMC algorithms for Bayesian variable selection in the logistic regression model for large-scale genomic applications
Manuela Zucknick, Sylvia Richardson
In large-scale genomic applications vast numbers of molecular features are scanned in order to find a small number of candidates which are linked to a particular disease or phenoty…