Physically-inspired Gaussian process models for post-transcriptional regulation in Drosophila

Lopez-Lopera, A.F., Durrande, N. and Alvarez, M.A. (2021) Physically-inspired Gaussian process models for post-transcriptional regulation in Drosophila. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18 (2). pp. 656-666. ISSN 1545-5963

Abstract

Metadata

Authors/Creators:
  • Lopez-Lopera, A.F.
  • Durrande, N.
  • Alvarez, M.A.
Copyright, Publisher and Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy.
Keywords: Mathematical model; diffusion equation; regulatory networks; stochastic processes; gap gene expression data
Dates:
  • Accepted: 18 May 2019
  • Published (online): 27 May 2019
  • Published: 1 April 2021
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Funding Information:
FunderGrant number
Engineering and Physical Science Research Council (EPSRC)EP/R034303/1
Depositing User: Symplectic Sheffield
Date Deposited: 30 Sep 2019 15:04
Last Modified: 12 May 2021 07:25
Status: Published
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
Identification Number: https://doi.org/10.1109/tcbb.2019.2918774
Related URLs:

Share / Export

Statistics