# HiDi: An efficient reverse engineering schema for large scale dynamic regulatory network reconstruction using adaptive differentiation

## TL;DR

HiDi introduces a scalable and accurate method for large-scale gene regulatory network reconstruction using adaptive differentiation and pre-filtration, outperforming existing approaches on benchmark datasets.

## Contribution

The paper presents a novel linear differential equation model with adaptive numerical differentiation and pre-filtration, enabling scalable and accurate network inference.

## Key findings

- Outperforms state-of-the-art methods on DREAM datasets
- Displays linear computation time with increasing network size
- Maintains high accuracy under noisy data conditions

## Abstract

The use of differential equations (ODE) is one of the most promising approaches to network inference. The success of ODE-based approaches has, however, been limited, due to the difficulty in estimating parameters and by their lack of scalability. Here we introduce a novel method and pipeline to reverse engineer gene regulatory networks from gene expression of time series and perturbation data based upon an improvement on the calculation scheme of the derivatives and a pre-filtration step to reduce the number of possible links. The method introduces a linear differential equation model with adaptive numerical differentiation that is scalable to extremely large regulatory networks. We demonstrate the ability of this method to outperform current state-of-the-art methods applied to experimental and synthetic data using test data from the DREAM4 and DREAM5 challenges. Our method displays greater accuracy and scalability. We benchmark the performance of the pipeline with respect to data set size and levels of noise. We show that the computation time is linear over various network sizes.