Influence of network topology and data collection on network inference.
Abstract
We recently developed an approach for testing the accuracy of network inference algorithms
by applying them to biologically realistic simulations with known network topology.
Here, we seek to determine the degree to which the network topology and data sampling
regime influence the ability of our Bayesian network inference algorithm, NETWORKINFERENCE,
to recover gene regulatory networks. NETWORKINFERENCE performed well at recovering
feedback loops and multiple targets of a regulator with small amounts of data, but
required more data to recover multiple regulators of a gene. When collecting the same
number of data samples at different intervals from the system, the best recovery was
produced by sampling intervals long enough such that sampling covered propagation
of regulation through the network but not so long such that intervals missed internal
dynamics. These results further elucidate the possibilities and limitations of network
inference based on biological data.
Type
Journal articleSubject
AlgorithmsAnimals
Bayes Theorem
Computational Biology
Computer Simulation
Gene Expression Profiling
Gene Expression Regulation
Humans
Models, Genetic
Songbirds
Vocalization, Animal
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https://hdl.handle.net/10161/11221Collections
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Show full item recordScholars@Duke
Alexander J. Hartemink
Professor in the Department of Computer Science
Computational biology, machine learning, Bayesian statistics, transcriptional regulation,
genomics and epigenomics, graphical models, Bayesian networks, hidden Markov models, systems
biology, computational neurobiology, classification, feature selection
Erich David Jarvis
Adjunct Professor in the Dept. of Neurobiology
Dr. Jarvis' laboratory studies the neurobiology of vocal communication. Emphasis is
placed on the molecular pathways involved in the perception and production of learned
vocalizations. They use an integrative approach that combines behavioral, anatomical,
electrophysiological and molecular biological techniques. The main animal model used
is songbirds, one of the few vertebrate groups that evolved the ability to learn vocalizations.
The generality of the discoveries is tested in other vocal
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