# How accurate is mechanobiology? A statistical test of cell force

arXiv: https://arxiv.org/abs/2412.18406 · HTML: https://mlsanity.com/paper/2412.18406/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 86 references that are in the map.
- First posted on arXiv 24 Dec 2024.

## Builds on (most cited first, 3 of 3)

- [On instabilities of deep learning in image reconstruction - Does AI come at a cost?](https://mlsanity.com/paper/1902.05300/) (2019)
- [Optimal low-rank approximations of Bayesian linear inverse problems](https://mlsanity.com/paper/1407.3463/) (2014)
- [P-value: A Bless or A Curse for Evidence-Based Studies?](https://mlsanity.com/paper/1809.08503/) (2018)

## Abstract (arXiv)

Mechanobiology is gaining more and more traction as the fundamental role of physical forces in biological function becomes clearer. Forces at the microscale are often measured indirectly using inverse problems such as Traction Force Microscopy because biological experiments are hard to access with physical probes. In contrast with the experimental nature of biology and physics, these measurements do not come with error bars, confidence regions, or p-values. The aim of this manuscript is to publicize this issue and to propose a first step towards a remedy therefor in the form of a general reconstruction framework. We also show that this opens the door to hypothesis testing of seemingly abstract experimental questions.