# Pruning neural network models for gene regulatory dynamics using data and domain knowledge

Source: [Pruning neural network models for gene regulatory dynamics using data and domain knowledge](<https://www.sota2.com/research/paper/68fa5f6e1e209131ce9e9cf7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-04T04:49:18.048Z

Authors: Intekhab Hossain, Jonas Fischer, Rebekka Burkholz, John Quackenbush

Published: 2024-03-05T23:02:55.000Z

## Abstract

The practical utility of machine learning models in the sciences often hinges on their interpretability. It is common to assess a model's merit for scientific discovery, and thus novel insights, by how well it aligns with already available domain knowledge--a dimension that is currently largely disregarded in the comparison of neural network models. While pruning can simplify deep neural network architectures and excels in identifying sparse models, as we show in the context of gene regulatory network inference, state-of-the-art techniques struggle with biologically meaningful structure learning. To address this issue, we propose DASH, a generalizable framework that guides network pruning by using domain-specific structural information in model fitting and leads to sparser, better interpretable models that are more robust to noise. Using both synthetic data with ground truth information, as well as real-world gene expression data, we show that DASH, using knowledge about gene interaction partners within the putative regulatory network, outperforms general pruning methods by a large margin and yields deeper insights into the biological systems being studied.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Gene regulatory dynamics prediction on SIM350 5% noise (test)](<https://www.sota2.com/research/sota/gene-regulatory-dynamics-prediction-on-sim350-5-percent-noise-test.md>): Gene regulatory dynamics prediction; SIM350 5% noise (test); MSE: 2.3.

- [Gene Regulatory Network Inference on Yeast cell cycle](<https://www.sota2.com/research/sota/gene-regulatory-network-inference-on-yeast-cell-cycle.md>): Gene regulatory network inference; Yeast cell cycle; Sparsity: 97.18.

- [Gene Regulatory Network Inference on SIM350 5% noise (test)](<https://www.sota2.com/research/sota/gene-regulatory-network-inference-on-sim350-5-percent-noise-test.md>): Gene regulatory network inference; SIM350 5% noise (test); Sparsity: 94.6.

- [Gene Regulatory Network Inference on Breast cancer in pseudotime](<https://www.sota2.com/research/sota/gene-regulatory-network-inference-on-breast-cancer-in-pseudotime.md>): Gene regulatory network inference; Breast cancer in pseudotime; Sparsity: 93.44.

- [Gene expression dynamics prediction on Hematopoiesis Erythroid lineage (test)](<https://www.sota2.com/research/sota/gene-expression-dynamics-prediction-on-hematopoiesis-erythroid-lineage-test.md>): Gene expression dynamics prediction; Hematopoesis Erythroid lineage (test); Sparsity: 0.8709.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2403.04805>)

[PDF](<https://arxiv.org/pdf/2403.04805.pdf>)

[Implementation](<https://github.com/QuackenbushLab/DASH>)