A huge thanks to my fantastic multi-disciplinary co-authors: Vladimir A. Shitov, Malte Lücken, Niki Kilbertus, Stefan Bauer, Marie Piraud, Alena Buyx, and Fabian Theis.

To fighting biases in ML-based single-cell science!

Our work traces these biases’ origins and interactions across the development pipeline. This pipeline-informed approach highlights how biases interconnect, potentially amplifying their impacts and complicating mitigation efforts.

Biases arise at every step of the ML-based single-cell analysis pipeline. We highlight:
🏥 Clinical Biases
👥 Cohort biases
🧪 Biases introduced during single-cell sequencing
🤖 Machine-learning and interpretational biases specific to weakly supervised or unsupervised ML models

A brief TL;DR:
Recent advances in ML-based single-cell data science offer groundbreaking insights into human health, enabling the stratification of tissue donors at single-cell resolution. But these insights are not immune to biases that can compromise their generalizability and fairness.

How do biases affect machine-learning models of human single-cell data? And what can we do about it? In our new Perspective article, "Biases in machine-learning models of human single-cell data," published in Nature Cell Biology, we explore these pressing questions.

👉🏻 www.nature.com/articles/s41...

Biases arise at every step of the ML-based single-cell analysis pipeline. We highlight:
🏥 Clinical Biases
👥 Cohort biases
🧪 Biases introduced during single-cell sequencing
🤖 Machine-learning and interpretational biases specific to weakly supervised or unsupervised ML models

A brief TL;DR:
Recent advances in ML-based single-cell data science offer groundbreaking insights into human health, enabling the stratification of tissue donors at single-cell resolution. But these insights are not immune to biases that can compromise their generalizability and fairness.

The Embedded Ethics and Social Science Approach: In the past years, our team at @ihemtum.bsky.social @alenabuyx.bsky.social together with colleagues at the TUM School of Social Sciences and Technology, have been developing the Embedded Ethics and Social Science approach.

🔊🧪New publication by @theresawillem.bsky.social et al. out in JMIR medical informatics: "The social construction of categorical data: A mixed-methods approach to assessing data features in publicly available machine learning datasets"
👉 Full paper, open access: medinform.jmir.org/2025/1/e59452