Alireza Karbalayghareh
ABSTRACT
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.
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Digital Library
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- A. Karbalayghareh, U. Braga-Neto, and E. R. Dougherty. Submitted. Classification of Gene Expression Trajectories from Incomplete and Noisy Data. IEEE/ACM Transactions on Computational Biology and Bioinformatics ( Submitted).Google Scholar
- A. Karbalayghareh, U. Braga-Neto, J. Hua, and E. R. Dougherty 2016. Classification of State Trajectories in Gene Regulatory Networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. PP, 99 (2016), 1--1.Google Scholar
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Index Terms
- Intrinsically Bayesian Robust Classifier for Single-Cell Gene Expression Time Series in Gene Regulatory Networks
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