Ensemble learning is an intensively studied technique in machine learning and pattern
recognition. Recent work in computational biology has seen an increasing use of ensemble …

A major challenge of the post-genomics era is to define the connectivity of protein
phosphorylation networks. Here, we quantitatively delineate the insulin signaling network in …

Exercise is essential in regulating energy metabolism and whole-body insulin sensitivity. To
explore the exercise signaling network, we undertook a global analysis of protein …

The remarkable flexibility and adaptability of ensemble methods and deep learning models
have led to the proliferation of their application in bioinformatics research. Traditionally, …

Mammary stem/progenitor cells (MaSCs) maintain self-renewal of the mammary epithelium
during puberty and pregnancy. DNA methylation provides a potential epigenetic mechanism …

Cell type-specific master transcription factors (TFs) play vital roles in defining cell identity
and function. However, the roles ubiquitous factors play in the specification of cell identity …

Background A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately
detect the number of cell types in the sample, which can be critical for downstream analyses …

mRNA alternative polyadenylation (APA) plays a critical role in post‐transcriptional gene
control and is highly regulated during development and disease. However, the regulatory …

Bivalent chromatin is characterized by the simultaneous presence of H3K4me3 and H3K27me3,
histone modifications generally associated with transcriptionally active and repressed …

Recent advances in single-cell biotechnologies have resulted in high-dimensional datasets
with increased complexity, making feature selection an essential technique for single-cell …