Our understanding of gene expression has changed dramatically over the past decade,
largely catalysed by technological developments. High-throughput experiments …

With the rapid advances of various high-throughput technologies, generation of '-omics' data
is commonplace in almost every biomedical field. Effective data management and analytical …

Background An appropriate sample size is essential for obtaining a precise and reliable
outcome of a study. In machine learning (ML), studies with inadequate samples suffer from …

INTRODUCTION Cancer immunotherapies that target tumor-specific T cells have benefited
many cancer patients, but the clinical efficacy varies greatly among different cancer types …

Fibroblasts, known for their functional diversity, play crucial roles in inflammation and
cancer. In this study, we conduct comprehensive single-cell RNA sequencing analyses on …

Background Network meta-analysis is used to compare three or more treatments for the
same condition. Within a Bayesian framework, for each treatment the probability of being …

Parkinson's disease (PD) is a major neurodegenerative chronic disease, most likely caused
by a complex interplay of genetic and environmental factors. Information on various aspects …

The increasing use of high-throughput gene expression quantification technologies over the
last two decades and the fact that most of the published studies are stored in public …

The widespread applications of various 'omics' technologies in biomedical research together
with the emergence of public data repositories have resulted in a plethora of data sets for …

Background High-throughput sequencing is now regularly used for studies of the
transcriptome (RNA-seq), particularly for comparisons among experimental conditions. For …