Summary of Identifying Perturbation Targets Through Causal Differential Networks, by Menghua Wu et al.
Identifying perturbation targets through causal differential networks
by Menghua Wu, Umesh Padia, Sean H. Murphy, Regina Barzilay, Tommi Jaakkola
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper proposes a novel approach to identify the variables responsible for changes in biological systems, which has applications in drug target discovery and cell engineering. The goal is to isolate the subset of observed variables that were targeted by an intervention, given observational and interventional datasets. However, directly applying causal discovery algorithms is challenging due to the large number of variables and limited samples per intervention. To address this, the authors propose a causality-inspired approach that infers noisy causal graphs from the data and then learns to map these differences to intervened-upon variables. The approach is trained on simulated and real data and outperforms baselines for perturbation modeling on seven single-cell transcriptomics datasets. It also demonstrates significant improvements over current causal discovery methods for predicting soft and hard intervention targets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how biological systems change in response to interventions, which can help discover new drugs and engineer cells. The challenge is that we have lots of data with few examples of what happened when we intervened. To solve this, the authors develop a new approach that looks at the differences between the data before and after an intervention. They test their method on real and fake data and show it works better than other methods for predicting what was targeted by an intervention. |
