# JUMP-lite: Compact, reproducible benchmarking of cell representations

arXiv: https://arxiv.org/abs/2608.07632 · HTML: https://mlsanity.com/paper/2608.07632/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 48 references that are in the map.
- First posted on arXiv 7 Aug 2026.

## Builds on (most cited first, 12 of 12)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology](https://mlsanity.com/paper/2404.10242/) (2024)
- [RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods](https://mlsanity.com/paper/2301.05768/) (2023)
- [SDRBench: Scientific Data Reduction Benchmark for Lossy Compressors](https://mlsanity.com/paper/2101.03201/) (2021)
- [CHAMMI: A benchmark for channel-adaptive models in microscopy imaging](https://mlsanity.com/paper/2310.19224/) (2023)
- [ChAda-ViT : Channel Adaptive Attention for Joint Representation Learning of Heterogeneous Microscopy Images](https://mlsanity.com/paper/2311.15264/) (2023)
- [RxRx3-core: Benchmarking drug-target interactions in High-Content Microscopy](https://mlsanity.com/paper/2503.20158/) (2025)
- [MOTIVE: A Drug-Target Interaction Graph For Inductive Link Prediction](https://mlsanity.com/paper/2406.08649/) (2024)
- [cp_measure: API-first feature extraction for image-based profiling workflows](https://mlsanity.com/paper/2507.01163/) (2025)
- [Deep learning based Image Compression for Microscopy Images: An Empirical Study](https://mlsanity.com/paper/2311.01352/) (2023)
- [Efficient Cell Painting Image Representation Learning via Cross-Well Aligned Masked Siamese Network](https://mlsanity.com/paper/2509.19896/) (2025)
- [Lossy Compression of Scientific Data: Applications Constrains and Requirements](https://mlsanity.com/paper/2503.20031/) (2025)

## Built on by (most cited first, 1 of 1)

- [Can phenotypic activity be predicted without experimental readouts?](https://mlsanity.com/paper/2610.07997/) (2026)

## Abstract (arXiv)

Image-based profiling captures rich phenotypic signatures for drug discovery and functional genomics. Large public datasets like JUMP Cell Painting now provide millions of images for systematic study. However, JUMP alone occupies 115 TB, and fragmented evaluation practices make systematic comparisons of representation methods impractical for many researchers. Here we present Nahual, an open-source framework for reproducible model deployment, and JUMP-lite, a 92.0 GB subset of JUMP that is approximately 1,250-fold smaller, selected to cover genetic modalities and compound annotations and reduced via lossy JPEG XL compression. Using these resources, we benchmark five representation methods, including classical features (CellProfiler) and deep learning models (MorphEM, OpenPhenom, SubCell, DINOv2). Moderate compression broadly retains signal relative to uncompressed images. Standardized phenotypic activity and consistency metrics reveal meaningful performance differences across methods. Together, JUMP-lite and Nahual provide a foundation for accessible, reproducible benchmarking of image-based cell representations.