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Ditzler G, Bouaynaya N, Shterenberg R, et al. Approximate kernel reconstruction for time-varying networks. BioData Min. 2019;12:5doi: 10.1186/s13040-019-0192-1.
Ditzler, G., Bouaynaya, N., Shterenberg, R., & Fathallah-Shaykh, H. M. (2019). Approximate kernel reconstruction for time-varying networks. BioData mining, 125. https://doi.org/10.1186/s13040-019-0192-1
Ditzler, Gregory, et al. "Approximate kernel reconstruction for time-varying networks." BioData mining vol. 12 (2019): 5. doi: https://doi.org/10.1186/s13040-019-0192-1
Ditzler G, Bouaynaya N, Shterenberg R, Fathallah-Shaykh HM. Approximate kernel reconstruction for time-varying networks. BioData Min. 2019 Feb 06;12:5. doi: 10.1186/s13040-019-0192-1. eCollection 2019. PMID: 30774716; PMCID: PMC6364395.
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BioData Min. 2019 Feb 06;12:5. doi: 10.1186/s13040-019-0192-1. eCollection 2019.

Approximate kernel reconstruction for time-varying networks.

BioData mining

Gregory Ditzler, Nidhal Bouaynaya, Roman Shterenberg, Hassan M Fathallah-Shaykh

Affiliations

  1. 1Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ USA.
  2. 2Department of Electrical and Computer Engineering, Rowan University, Glassboro, NJ USA.
  3. 3Department of Mathematics, University of Alabama at Birmingham, Birmingham, AL USA.
  4. 4School of Medicine, University of Alabama at Birmingham, Birmingham, AL USA.

PMID: 30774716 PMCID: PMC6364395 DOI: 10.1186/s13040-019-0192-1

Abstract

BACKGROUND: Most existing algorithms for modeling and analyzing molecular networks assume a static or time-invariant network topology. Such view, however, does not render the temporal evolution of the underlying biological process as molecular networks are typically "re-wired" over time in response to cellular development and environmental changes. In our previous work, we formulated the inference of time-varying or dynamic networks as a tracking problem, where the target state is the ensemble of edges in the network. We used the Kalman filter to track the network topology over time. Unfortunately, the output of the Kalman filter does not reflect known properties of molecular networks, such as sparsity.

RESULTS: To address the problem of inferring sparse time-varying networks from a set of under-sampled measurements, we propose the Approximate Kernel RecONstruction (AKRON) Kalman filter. AKRON supersedes the Lasso regularization by starting from the Lasso-Kalman inferred network and judiciously searching the space for a sparser solution. We derive theoretical bounds for the optimality of AKRON. We evaluate our approach against the Lasso-Kalman filter on synthetic data. The results show that not only does AKRON-Kalman provide better reconstruction errors, but it is also better at identifying if edges exist within a network. Furthermore, we perform a real-world benchmark on the lifecycle (embryonic, larval, pupal, and adult stages) of the

CONCLUSIONS: We show that the networks inferred by the AKRON-Kalman filter are sparse and can detect more known gene-to-gene interactions for the

Keywords: Compressive sensing; Gene regulatory; Gene regulatory networks; Time-varying network

Conflict of interest statement

Not applicable.Not applicable.The authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affilia

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