Constructing gene regulatory networks is a critical step in revealing disease mechanisms from transcriptomic data. In this work, we present NO-BEARS, a novel algorithm for estimating gene regulatory networks. The NO-BEARS algorithm is built on the basis of the NOTEARS algorithm with two improvements. First, we propose a new constraint and its fast approximation to reduce the computational cost of the NO-TEARS algorithm. Next, we introduce a polynomial regression loss to handle non-linearity in gene expressions. Our implementation utilizes modern GPU computation that can decrease the time of hours-long CPU computation to seconds. Using synthetic data, we demonstrate improved performance, both in processing time and accuracy, on inferring gene regulatory networks from gene expression data.
@article{arxiv.1911.00081,
title = {Scaling structural learning with NO-BEARS to infer causal transcriptome networks},
author = {Hao-Chih Lee and Matteo Danieletto and Riccardo Miotto and Sarah T. Cherng and Joel T. Dudley},
journal= {arXiv preprint arXiv:1911.00081},
year = {2019}
}
Comments
Preprint of an article submitted for consideration in Pacific Symposium on Biocomputing copyright 2019 World Scientific Publishing Co., Singapore, http://psb.stanford.edu/http://psb.stanford.edu/