Gene Regulatory Network Inference as Relaxed Graph Matching

Authors

  • Deborah Weighill Harvard T.H. Chan School of Public Health, Boston, MA 02115
  • Marouen Ben Guebila Harvard T.H. Chan School of Public Health, Boston, MA 02115
  • Camila Lopes-Ramos Harvard T.H. Chan School of Public Health, Boston, MA 02115
  • Kimberly Glass Harvard T.H. Chan School of Public Health, Boston, MA 02115 Channing Division of Network Medicine, Brigham and Women’s Hospital Harvard Medical School, Boston, MA 02115
  • John Quackenbush Harvard T.H. Chan School of Public Health, Boston, MA 02115 Channing Division of Network Medicine, Brigham and Women’s Hospital Harvard Medical School, Boston, MA 02115
  • John Platig Channing Division of Network Medicine, Brigham and Women’s Hospital Harvard Medical School, Boston, MA 02115
  • Rebekka Burkholz Harvard T.H. Chan School of Public Health, Boston, MA 02115

Keywords:

Graph-based Machine Learning, Bioinformatics

Abstract

Bipartite network inference is a ubiquitous problem across disciplines. One important example in the field molecular biology is gene regulatory network inference. Gene regulatory networks are an instrumental tool aiding in the discovery of the molecular mechanisms driving diverse diseases, including cancer. However, only noisy observations of the projections of these regulatory networks are typically assayed. In an effort to better estimate regulatory networks from their noisy projections, we formulate a non-convex but analytically tractable optimization problem called OTTER. This problem can be interpreted as relaxed graph matching between the two projections of the bipartite network. OTTER's solutions can be derived explicitly and inspire a spectral algorithm, for which we provide network recovery guarantees. We also provide an alternative approach based on gradient descent that is more robust to noise compared to the spectral algorithm. Interestingly, this gradient descent approach resembles the message passing equations of an established gene regulatory network inference method, PANDA. Using three cancer-related data sets, we show that OTTER outperforms state-of-the-art inference methods in predicting transcription factor binding to gene regulatory regions. To encourage new graph matching applications to this problem, we have made all networks and validation data publicly available.

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Published

2021-05-18

How to Cite

Weighill, D., Ben Guebila, M., Lopes-Ramos, C., Glass, K., Quackenbush, J., Platig, J., & Burkholz, R. (2021). Gene Regulatory Network Inference as Relaxed Graph Matching. Proceedings of the AAAI Conference on Artificial Intelligence, 35(11), 10263-10272. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17230

Issue

Section

AAAI Technical Track on Machine Learning IV