Rapid growth of high-dimensional datasets in fields such as single-cell RNA sequencing
and spatial genomics has led to unprecedented opportunities for scientific discovery, but it …

Manifold learning techniques have emerged as crucial tools for uncovering latent patterns in
high-dimensional single-cell data. However, most existing dimensionality reduction methods …

Big neuroscience datasets are not big small datasets when it comes to quantitative data
analysis. Neuroscience has now witnessed the advent of many population cohort studies …

In this paper, we propose Graph Differential Equation Network (GDeNet), an approach that
harnesses the expressive power of solutions to PDEs on a graph to obtain continuous node …

The application of large-language models (LLMs) to single-cell gene-expression data has
introduced a new type of data that includes a gene-embedding matrix, in addition to the …

Analysis of single-cell RNA sequencing data is often conducted through network projections
such as coexpression networks, primarily due to the abundant availability of network …

Deriving meaningful representations from complex, high-dimensional data in unsupervised
settings is crucial across diverse machine learning applications. This paper introduces a …

Given a bounded domain, we deal with the problem of estimating the distance function from
the internal points of the domain to the boundary of the domain. Convolutional and …

The “curse of dimensionality” is a major concern in the field of computational biology,
especially when there are many fewer samples then to the number of features. Many …