NDLI: Intrinsically Bayesian Robust Classifier for Single-Cell Gene Expression Time Series in Gene Regulatory Networks

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Intrinsically Bayesian Robust Classifier for Single-Cell Gene Expression Time Series in Gene Regulatory Networks

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Intrinsically Bayesian Robust Classifier for Single-Cell Gene Expression Time Series in Gene Regulatory Networks

Content Provider	ACM Digital Library
Author	Dougherty, Edward R. Braga-Neto, Ulisses Karbalayghareh, Alireza
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.
Starting Page	766
Ending Page	767
Page Count	2
File Format	PDF
ISBN	9781450347228
DOI	10.1145/3107411.3110408
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-08-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Bayesian partially observed boolean dynamical system Optimal bayesian classifier Bayesian trajectory classifier Intrinsically bayesian robust classifier Gene regulatory network Single-cell expression trajectory Boolean network
Content Type	Text
Resource Type	Article

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