Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space,
perform particularly well in various tasks that process graph structure data. With the rapid …

Biological networks are powerful resources for the discovery of genes and genetic modules
that drive disease. Fundamental to network analysis is the concept that genes underlying the …

Proteins and their functional interactions form the backbone of the cellular machinery. Their
connectivity network needs to be considered for the full understanding of biological …

Transcription factors (TFs) are major trans-acting factors in transcriptional regulation.
Therefore, elucidating TF–target interactions is a key step toward understanding the …

A system-wide understanding of cellular function requires knowledge of all functional
interactions between the expressed proteins. The STRING database aims to collect and …

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Here, we
describe the genomic landscape of 496 PTCs. We observed a low frequency of somatic …

Genome-scale human protein–protein interaction networks are critical to understanding cell
biology and interpreting genomic data, but challenging to produce experimentally. Through …

Alternative splicing changes are frequently observed in cancer and are starting to be
recognized as important signatures for tumor progression and therapy. However, their …

Network medicine has proven useful for dissecting genetic organization of complex human
diseases. We have previously published HumanNet, an integrated network of human genes …

INTRODUCTION Transcription factors (TFs) are often studied one by one or in clusters of a
few related factors. However, the integration and networks of transcriptional changes to …