Summary of Inferring Biological Processes with Intrinsic Noise From Cross-sectional Data, by Suryanarayana Maddu et al.
Inferring biological processes with intrinsic noise from cross-sectional data
by Suryanarayana Maddu, Victor Chardès, Michael. J. Shelley
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Biological Physics (physics.bio-ph); Quantitative Methods (q-bio.QM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed probability flow inference (PFI) method addresses the challenge of inferring dynamical models from omics data by disentangling force from intrinsic stochasticity. The approach infers phase-space probability flows that share time-dependent marginal distributions with the underlying stochastic process, allowing for accurate parameter and force estimation in high-dimensional stochastic reaction networks. PFI outperforms state-of-the-art methods in inferring cell differentiation dynamics with molecular noise. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists are trying to figure out how biological processes work. They have some data from different time points, but it’s hard to understand because there is a lot of randomness involved. The new method called probability flow inference (PFI) helps solve this problem by looking at the flow of probabilities in a special way. This makes it easier to understand how biological processes work and what forces are driving them. PFI works well even when there is a lot of noise in the data, which is important for studying things like cell differentiation. |
Keywords
» Artificial intelligence » Inference » Probability
