Yashvardhan Jain

Multiscale harmonization, semantic integration, and immersive exploration of single-cell data in support of novel biological insights

A free Meta Quest virtual reality app harmonizes CODEX, Visium, Xenium, volumetric immunofluorescence and electron microscopy data from 5 organs into 4 scale levels using the Human Reference Atlas.

Bridging Biomedical Atlas Ecosystem: Cross-Atlas Alignment And Scalable Tissue Specimen Registration

The paper introduces methods to scale up the Human Reference Atlas by aligning tissue datasets across reference systems and using millitomes for bulk registration.

Vasculature segmentation in 3D hierarchical phase-contrast tomography images of human kidneys

The paper presents a benchmark for 3D blood vessel segmentation in human kidney phase-contrast tomography images using top-performing deep learning solutions from a global competition.

SenNet Portal: Build, Optimization and Usage

The paper introduces the SenNet Portal, a resource for senescent cell data and tools, and discusses its usage and future developments.

Cell type populations for 3D anatomical structures of the Human Reference Atlas

The paper quantifies cell type populations across 3D anatomical structures using single-cell transcriptomics and proteomics data from the Human Reference Atlas.

Exploring endothelial cell environments across organs in spatially resolved omics data

The paper analyzes endothelial cell environments across organs using spatially resolved omics data and provides an interactive tool for visualizing cell distances and neighborhoods.

Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage

This paper describes the construction and usage of the Human Reference Atlas (HRA) v2.0, which integrates multiscale data across 36 organs and provides tools for analysis and visualization.

Generalized cell phenotyping for spatial proteomics with language-informed vision models

This paper introduces DeepCell Types, a language-informed vision model for spatial proteomics cell phenotyping that generalizes across diverse datasets and marker panels.

Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms

The paper describes a machine learning competition to develop automated image segmentation tools for anatomical structures across human organs, resulting in models that will be used in the Human Reference Atlas.

Robust and generalizable segmentation of human functional tissue units

The paper evaluates five algorithms for segmenting functional tissue units in kidney and colon tissues using histological stains, comparing their accuracy and generalizability.