Scaling structural learning with NO-BEARS to infer causal transcriptome networks

Hao Chih Lee, Matteo Danieletto, Riccardo Miotto, Sarah T. Cherng, Joel T. Dudley

Research output: Contribution to journalConference articlepeer-review

11 Scopus citations


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 NO-TEARS 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.

Original languageEnglish
Pages (from-to)391-402
Number of pages12
JournalPacific Symposium on Biocomputing
Issue number2020
StatePublished - 2020
Event25th Pacific Symposium on Biocomputing, PSB 2020 - Big Island, United States
Duration: 3 Jan 20207 Jan 2020


  • Bayesian network
  • GPU acceleration
  • Gene regulatory network
  • Optimization


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