Nature Biotechnology - Matched treatment and control single-cell RNA sequencing samples are more accurately compared at the single-cell level.

Nature Biotechnology - PHATE, a new data visualization tool, better preserves patterns in high-dimensional data after dimensionality reduction.

Nature Machine Intelligence - The space of possible proteins is vast, and optimizing proteins for specific target properties computationally is an ongoing challenge, even with large amounts of...

Nature Biotechnology - PHATE, a new data visualization tool, better preserves patterns in high-dimensional data after dimensionality reduction.

While single-cell technologies have allowed scientists to characterize cell states that emerge during cancer progression through temporal sampling, connecting these samples over time and inferring...

Nature Methods - SAUCIE, a deep learning platform to analyze single-cell data across samples and platforms, allows information to be obtained from the internal layers of the network, which provides...

Nature Computational Science - A manifold learning method called T-PHATE is developed for high-dimensional time-series data. T-PHATE is applied to brain data (functional magnetic resonance imaging)...

A new algorithm overcomes limitations of data loss in single-cell sequencing experiments.

We propose a new fast method of measuring distances between large numbers of related high dimensional datasets called the Diffusion Earth Mover's Distance (EMD). We model the datasets as...

The biomedical community is producing increasingly high dimensional datasets, integrated from hundreds of patient samples, which current computational techniques struggle to explore. To uncover...

Abstract. Diffusion condensation is a dynamic process that yields a sequence of multiscale data representations that aim to encode meaningful abstractions. It has proven effective for manifold...

We introduce a new intrinsic measure of local curvature on point-cloud data called diffusion curvature. Our measure uses the framework of diffusion maps, including the data diffusion operator, to...

It is increasingly common to encounter data from dynamic processes captured by static cross-sectional measurements over time, particularly in biomedical settings. Recent attempts to model...

Read the paper: https://arxiv.org/abs/1908.02831Understanding why and how certain neural networks outperform others is key to guiding future development of n...

It is increasingly common to encounter data from dynamic processes captured by static cross-sectional measurements over time, particularly in biomedical settings. Recent attempts to model...

Deep thoughts on geometric learning & exploration of non-Euclidean data Guy Wolf and I are organizing an online SIAM Mathematics of Data Science (MDS) mini-symposium on “Deep thoughts on …

Since COVID-19 hit our state two years ago, more than 10,000 residents have died, but what if there was a scientific way to predict who would live and who would die? A team of researchers at Yale...