Aim Timely recruitment of eligible participants is essential for the success of clinical trials,
with insufficient accrual being the leading cause for premature termination of both oncology …

Integrative analysis of multi-omics data has the potential to yield valuable and
comprehensive insights into the molecular mechanisms underlying complex diseases such …

There has been an increasing interest in decomposing high-dimensional multi-omics data
into a product of low-rank and sparse matrices for the purpose of dimension reduction and …

There is a growing body of literature on factor analysis that can capture individual and
shared structures in multi-modal data. However, few of these approaches incorporate …

There is a growing body of literature on knowledge-guided statistical learning methods for
analysis of structured high-dimensional data (such as genomic and transcriptomic data) that …

In the past few decades, we have witnessed tremendous advancements in biology, life
sciences and healthcare. These advancements are due in no small part to the big data …

Identifying relevant genomic features that can act as prognostic markers for building
predictive survival models is one of the central themes in medical research, affecting the …

Real-world data (RWD) and real-world evidence (RWE) have been increasingly used in
medical product development and regulatory decision-making, especially for rare diseases …

Integrative learning of multiple datasets has the potential to mitigate the challenge of small
nn and large pp that is often encountered in analysis of big biomedical data such as …

Support vector machine (SVM) is a powerful classification tool for analysis of high
dimensional data such as genomics. Regularized linear and nonlinear SVM methods with …