Distortions from traditional dimensionality reduction methods obscure relationships in high-dimensional single-cell data, thus impeding biological insights. We introduce DTNE (diffusive topology ...

Differential network analysis plays an important role in learning how gene interactions change under different biological conditions, and the high resolution of single-cell RNA (scRNA-seq) sequencing ...

Non-negative matrix factorization, which decomposes the input non-negative matrix into product of two non-negative matrices, has been widely used in the neuroimaging field due to its flexible ...

Link prediction is a fundamental machine learning task on complex networks, used to evaluate the central technique of low-dimensional embeddings. Our results question the common wisdom that ...

Efforts to find disease genes using high-density single-nucleotide polymorphism (SNP) maps will produce data sets that exceed the limitations of current computational tools. Here we describe a new, ...