mlsanityData to 14 Oct 2026

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

arXiv 1911.00081 ↗First posted 31 Oct 2019q-bio.GN cs.LG stat.MLcited by 39

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Abstract (arXiv)

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.

Key facts. Cited by 39 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Oct 2019.

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Citing papers per year: 2021: 3; 2022: 4; 2023: 7; 2024: 11; 2025: 7; 2026: 7.

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  1. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 211
  2. 2021Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Gamecited by 161
  3. 2022Amortized Inference for Causal Structure Learningcited by 90
  4. 2022Large-Scale Differentiable Causal Discovery of Factor Graphscited by 47
  5. 2023Stable Differentiable Causal Discoverycited by 31
  6. 2022Truncated Matrix Power Iteration for Differentiable DAG Learningcited by 23
  7. 2023NODAGS-Flow: Nonlinear Cyclic Causal Structure Learningcited by 20
  8. 2024The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applicationscited by 18
  9. 2024Recursive Causal Discoverycited by 12
  10. 2023Constraint-Free Structure Learning with Smooth Acyclic Orientationscited by 10
  11. 2023Learning DAGs from Data with Few Root Causescited by 8
  12. 2024Markov Equivalence and Consistency in Differentiable Structure Learningcited by 7
27 more
  1. 2024Continuous Bayesian Model Selection for Multivariate Causal Discoverycited by 6
  2. 2025Does TabPFN Understand Causal Structures?cited by 5
  3. 2022Tearing Apart NOTEARS: Controlling the Graph Prediction via Variance Manipulationcited by 4
  4. 2024AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUscited by 4
  5. 2024Generative Intervention Models for Causal Perturbation Modelingcited by 4
  6. 2025Since Faithfulness Fails: The Performance Limits of Neural Causal Discoverycited by 4
  7. 2025Differentiable Cyclic Causal Discovery Under Unmeasured Confounderscited by 4
  8. 2023Differentiable Bayesian Structure Learning with Acyclicity Assurancecited by 3
  9. 2024Reinforcement Learning for Causal Discovery without Acyclicity Constraintscited by 3
  10. 2021Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicinecited by 2
  11. 2025Score-based Greedy Search for Structure Identification of Partially Observed Linear Causal Modelscited by 2
  12. 2023Recovering Linear Causal Models with Latent Variables via Cholesky Factorization of Covariance Matrixcited by 1
  13. 2023Multi-granularity Causal Structure Learningcited by 1
  14. 2024Learning Cyclic Causal Models from Incomplete Datacited by 1
  15. 2024Scalable Variational Causal Discovery Unconstrained by Acyclicitycited by 1
  16. 2024MissNODAG: Differentiable Cyclic Causal Graph Learning from Incomplete Datacited by 1
  17. 2025Towards Identifiability of Interventional Stochastic Differential Equationscited by 1
  18. 2026Unsupervised Causal Abstractions Discoverycited by 1
  19. 2024Learning Dynamic Bayesian Networks from Data: Foundations, First Principles and Numerical Comparisonscited by 0
  20. 2025CausalRec: A CausalBoost Attention Model for Sequential Recommendationcited by 0
  21. 2025Tab-PET: Graph-Based Positional Encodings for Tabular Transformerscited by 0
  22. 2026Scalable Contrastive Causal Discovery under Unknown Soft Interventionscited by 0
  23. 2026Smoothing the Landscape: Causal Structure Learning via Diffusion Denoising Objectivescited by 0
  24. 2026SCOUT: Cyclic Causal Discovery Under Soft Interventions with Unknown Targetscited by 0
  25. 2026Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selectioncited by 0
  26. 2026A Framework for Directed Acyclic Hypergraph Learningcited by 0
  27. 2026Amortized Bayesian Causal Discovery of Extended Factor Graphscited by 0