Intrinsically Bayesian Robust Classifier for Single-Cell Gene Expression Time Series in Gene Regulatory Networks Conference Paper uri icon

abstract

  • 2017 Copyright held by the owner/author(s). This paper studies expression-based classification under the assumption that single-cell measurements are sampled at a sufficient rate to detect regulatory timing. Observations are expression trajectories. In effect, classification is performed on data generated by an underlying gene regulatory network. Network regulation is modeled via a Boolean network with perturbation, regulation not being fully determined owing to inherent biological randomness, and we assume a partially known Gaussian observation model belonging to an uncertainty class of models. We derive the intrinsically Bayesian robust classifier to discriminate between wild-type and mutated networks based on expression trajectories. We test it using the mammalian cell-cycle model, discriminating between wild-type and mutated networks. Tests examine all model aspects, including trajectory length, perturbation probability, and the hyperparameters governing the prior distribution over the uncertainty class.

name of conference

  • Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

published proceedings

  • ACM-BCB' 2017: PROCEEDINGS OF THE 8TH ACM INTERNATIONAL CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY,AND HEALTH INFORMATICS

author list (cited authors)

  • Karbalayghareh, A., Braga-Neto, U., & Dougherty, E. R.

citation count

  • 1

complete list of authors

  • Karbalayghareh, Alireza||Braga-Neto, Ulisses||Dougherty, Edward R

publication date

  • January 2017