Removing the bias and variance of multicentre data has always been a challenge in large
scale digital healthcare studies, which requires the ability to integrate clinical features …

Batch effects in omics data are notoriously common technical variations unrelated to study
objectives, and may result in misleading outcomes if uncorrected, or hinder biomedical …

The benefit of integrating batches of genomic data to increase statistical power is often
hindered by batch effects, or unwanted variation in data caused by differences in technical …

Background Irritable bowel syndrome (IBS) is a common gastrointestinal disorder that is
thought to involve alterations in the gut microbiome, but robust microbial signatures have …

Heterogeneity and latent variables are now widely recognized as major sources of bias and
variability in high-throughput experiments. The most well-known source of latent variation in …

Site differences, or systematic differences in feature distributions across multiple data-
acquisition sites, are a known source of heterogeneity that may adversely affect large-scale …

Background Lung squamous cell carcinoma (LUSC) patients are often diagnosed at an
advanced stage and have poor prognoses. Thus, identifying novel biomarkers for the LUSC …

Background: Psychiatric disorders have been historically classified using symptom
information alone. Recently, there has been a dramatic increase in research interest not only …

Identifying germline BRCA1/2 mutation carriers is vital for reducing their risk of breast and
ovarian cancer. To derive a serum miRNA-based diagnostic test we used samples from 653 …

Tumor heterogeneity and the unclear metastasis mechanisms are the leading cause for the
unavailability of effective targeted therapy for Triple-negative breast cancer (TNBC), a breast …