Motivation Genomic data are often produced in batches due to practical restrictions, which
may lead to unwanted variation in data caused by discrepancies across batches. Such …

A one-shot federated transfer learning method using random forests (FTRF) is developed to
improve the prediction accuracy at a target data site by leveraging information from auxiliary …

We propose a general class of algorithms for estimating heterogeneous treatment effects on
multiple studies. Our approach, called the multi-study R-learner, generalizes the R-learner to …

It is increasingly common to encounter prediction tasks in the biomedical sciences for which
multiple datasets are available for model training. Common approaches such as pooling …

The aim of this paper is to systematically investigate merging and ensembling methods for
spatially varying coefficient mixed effects models (SVCMEM) in order to carry out integrative …

Heterogeneity in different genomic studies compromises the performance of machine
learning models in cross-study phenotype predictions. Overcoming heterogeneity when …

We propose the “study strap ensemble”, which combines advantages of two common
approaches to fitting prediction models when multiple training datasets (“studies”) are …

Motivation In the training of predictive models using high-dimensional genomic data,
multiple studies' worth of data are often combined to increase sample size and improve …

Multi-source domain adaptation (DA) aims at leveraging information from more than one
source domain to make predictions in a target domain, where different domains may have …

The innovative use of real‐world data (RWD) can answer questions that cannot be
addressed using data from randomized clinical trials (RCTs). While the sponsors of RCTs …