# Learning to refine domain knowledge for biological network inference

arXiv: https://arxiv.org/abs/2410.14436 · HTML: https://mlsanity.com/paper/2410.14436/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 18 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [DAGs with NO TEARS: Continuous Optimization for Structure Learning](https://mlsanity.com/paper/1803.01422/) (2018)
- [Axial Attention in Multidimensional Transformers](https://mlsanity.com/paper/1912.12180/) (2019)
- [Gradient-Based Neural DAG Learning](https://mlsanity.com/paper/1906.02226/) (2019)
- [Differentiable Causal Discovery from Interventional Data](https://mlsanity.com/paper/2007.01754/) (2020)
- [CAM: Causal additive models, high-dimensional order search and penalized regression](https://mlsanity.com/paper/1310.1533/) (2013)
- [Causal Inference in the Presence of Latent Variables and Selection Bias](https://mlsanity.com/paper/1302.4983/) (2013)
- [Permutation-based Causal Inference Algorithms with Interventions](https://mlsanity.com/paper/1705.10220/) (2017)
- [Large-Scale Differentiable Causal Discovery of Factor Graphs](https://mlsanity.com/paper/2206.07824/) (2022)
- [Scale-free networks are rare](https://mlsanity.com/paper/1801.03400/) (2018)
- [Sample, estimate, aggregate: A recipe for causal discovery foundation models](https://mlsanity.com/paper/2402.01929/) (2024)
- [Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View](https://mlsanity.com/paper/2403.15500/) (2024)

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

- [GPO-VAE: Modeling Explainable Gene Perturbation Responses utilizing GRN-Aligned Parameter Optimization](https://mlsanity.com/paper/2501.18973/) (2025)

## Abstract (arXiv)

Perturbation experiments allow biologists to discover causal relationships between variables of interest, but the sparsity and high dimensionality of these data pose significant challenges for causal structure learning algorithms. Biological knowledge graphs can bootstrap the inference of causal structures in these situations, but since they compile vastly diverse information, they can bias predictions towards well-studied systems. Alternatively, amortized causal structure learning algorithms encode inductive biases through data simulation and train supervised models to recapitulate these synthetic graphs. However, realistically simulating biology is arguably even harder than understanding a specific system. In this work, we take inspiration from both strategies and propose an amortized algorithm for refining domain knowledge, based on data observations. On real and synthetic datasets, we show that our approach outperforms baselines in recovering ground truth causal graphs and identifying errors in the prior knowledge with limited interventional data.