# Machine Learning for Uncovering Biological Insights in Spatial Transcriptomics Data

arXiv: https://arxiv.org/abs/2303.16725 · HTML: https://mlsanity.com/paper/2303.16725/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 95 references that are in the map.
- First posted on arXiv 29 Mar 2023.

## Builds on (most cited first, 7 of 7)

- [Interpretable machine learning: definitions, methods, and applications](https://mlsanity.com/paper/1901.04592/) (2019)
- [Stability](https://mlsanity.com/paper/1310.0150/) (2013)
- [Veridical Data Science](https://mlsanity.com/paper/1901.08152/) (2019)
- [Applications of Deep Learning and Reinforcement Learning to Biological Data](https://mlsanity.com/paper/1711.03985/) (2017)
- [Iterative Random Forests to detect predictive and stable high-order interactions](https://mlsanity.com/paper/1706.08457/) (2017)
- [Deep Learning in Mining Biological Data](https://mlsanity.com/paper/2003.00108/) (2020)
- [Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction](https://mlsanity.com/paper/1705.07585/) (2017)

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

- [Scalable Generation of Spatial Transcriptomics from Histology Images via Whole-Slide Flow Matching](https://mlsanity.com/paper/2506.05361/) (2025)

## Abstract (arXiv)

Development and homeostasis in multicellular systems both require exquisite control over spatial molecular pattern formation and maintenance. Advances in spatially-resolved and high-throughput molecular imaging methods such as multiplexed immunofluorescence and spatial transcriptomics (ST) provide exciting new opportunities to augment our fundamental understanding of these processes in health and disease. The large and complex datasets resulting from these techniques, particularly ST, have led to rapid development of innovative machine learning (ML) tools primarily based on deep learning techniques. These ML tools are now increasingly featured in integrated experimental and computational workflows to disentangle signals from noise in complex biological systems. However, it can be difficult to understand and balance the different implicit assumptions and methodologies of a rapidly expanding toolbox of analytical tools in ST. To address this, we summarize major ST analysis goals that ML can help address and current analysis trends. We also describe four major data science concepts and related heuristics that can help guide practitioners in their choices of the right tools for the right biological questions.