A new tool combines multiple types of data to better support the development of vaccines that target patient-specific pathology.

In this paper, we propose HiPoNet, an end-to-end differentiable neural network for regression, classification, and representation learning on high-dimensional point clouds. Our work is motivated by si...

Nature Biotechnology - Defining and benchmarking open problems in single-cell analysis

Brain-computer interfaces (BCIs) promise to restore and enhance a wide range of human capabilities. However, a barrier to the adoption of BCIs is how long it can take users to learn to control them. W...

We work on developing foundational mathematical <span class=

Learning meaningful representations of genes. Contribute to KrishnaswamyLab/GSPA-manuscript-analyses development by creating an account on GitHub.

This work presents a graph signal processing method, gene signal pattern analysis, to embed gene signals from single-cell sequencing data. In diverse experimental set-ups and case studies, GSPA establ...

This work presents a graph signal processing method, gene signal pattern analysis, to embed gene signals from single-cell sequencing data. In diverse experimental set-ups and case studies, GSPA establ...

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The proliferation of digital microscopy images, driven by advances in automated whole slide scanning, presents significant opportunities for biomedical research and clinical diagnostics. However, accu...

Segmenting medical images is critical to facilitating both patient diagnoses and quantitative research. A major limiting factor is the lack of labeled data, as obtaining expert annotations for each ne...

Advances in medical imaging technologies have enabled the collection of longitudinal images, which involve repeated scanning of the same patients over time, to monitor disease progression. However, pr...