A statistical framework for recovering pseudo-dynamic networks from static data

Author:

Chen Chixiang12,Shen Biyi3,Ma Tianzhou4ORCID,Wang Ming3,Wu Rongling3ORCID

Affiliation:

1. Division of Biostatistics and Bioinformatics, University of Maryland School of Medicine , Baltimore, MD 21201, USA

2. Department of Neurosurgery, University of Maryland School of Medicine , Baltimore, MD 21201, USA

3. Division of Biostatistics and Bioinformatics, College of Medicine, Pennsylvania State University , Hershey, PA 17033, USA

4. Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland , College Park, MD 20740, USA

Abstract

Abstract Motivation The collection of temporal or perturbed data is often a prerequisite for reconstructing dynamic networks in most cases. However, these types of data are seldom available for genomic studies in medicine, thus significantly limiting the use of dynamic networks to characterize the biological principles underlying human health and diseases. Results We proposed a statistical framework to recover disease risk-associated pseudo-dynamic networks (DRDNet) from steady-state data. We incorporated a varying coefficient model with multiple ordinary differential equations to learn a series of networks. We analyzed the publicly available Genotype-Tissue Expression data to construct networks associated with hypertension risk, and biological findings showed that key genes constituting these networks had pivotal and biologically relevant roles associated with the vascular system. We also provided the selection consistency of the proposed learning procedure and evaluated its utility through extensive simulations. Availability and implementation DRDNet is implemented in the R language, and the source codes are available at https://github.com/chencxxy28/DRDnet/. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

National Heart, Lung and Blood Institute

National Institute of Child Health and Human Development

National Institute of Health

National Center for Advancing Translational Sciences

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference34 articles.

1. The pathophysiology of cigarette smoking and cardiovascular disease: an update;Ambrose;J. Am. Coll. Cardiol,2004

2. Evolution of epistatic networks and the genetic basis of innate behaviors;Anholt;Trends Genet,2020

3. The genotype-tissue expression (GTEx) pilot analysis: multitissue gene regulation in humans;Ardlie;Science,2015

4. Network medicine: a network-based approach to human disease;Barabási;Nat. Rev. Genet,2011

5. Nonlinear parameter estimation: a case study comparison;Biegler;AIChE J,1986

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