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Advances to Bayesian network inference for generating causal networks from observational biological data.
Abstract
MOTIVATION: Network inference algorithms are powerful computational tools for identifying
putative causal interactions among variables from observational data. Bayesian network
inference algorithms hold particular promise in that they can capture linear, non-linear,
combinatorial, stochastic and other types of relationships among variables across
multiple levels of biological organization. However, challenges remain when applying
these algorithms to limited quantities of experimental data collected from biological
systems. Here, we use a simulation approach to make advances in our dynamic Bayesian
network (DBN) inference algorithm, especially in the context of limited quantities
of biological data. RESULTS: We test a range of scoring metrics and search heuristics
to find an effective algorithm configuration for evaluating our methodological advances.
We also identify sampling intervals and levels of data discretization that allow the
best recovery of the simulated networks. We develop a novel influence score for DBNs
that attempts to estimate both the sign (activation or repression) and relative magnitude
of interactions among variables. When faced with limited quantities of observational
data, combining our influence score with moderate data interpolation reduces a significant
portion of false positive interactions in the recovered networks. Together, our advances
allow DBN inference algorithms to be more effective in recovering biological networks
from experimentally collected data. AVAILABILITY: Source code and simulated data are
available upon request. SUPPLEMENTARY INFORMATION: http://www.jarvislab.net/Bioinformatics/BNAdvances/
Type
Journal articleSubject
AlgorithmsBayes Theorem
Computer Simulation
Gene Expression Profiling
Gene Expression Regulation
Models, Genetic
Models, Statistical
Oligonucleotide Array Sequence Analysis
Signal Transduction
Software
Permalink
https://hdl.handle.net/10161/11228Published Version (Please cite this version)
10.1093/bioinformatics/bth448Publication Info
Yu, Jing; Smith, V Anne; Wang, Paul P; Hartemink, Alexander J; & Jarvis, Erich D (2004). Advances to Bayesian network inference for generating causal networks from observational
biological data. Bioinformatics, 20(18). pp. 3594-3603. 10.1093/bioinformatics/bth448. Retrieved from https://hdl.handle.net/10161/11228.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Alexander J. Hartemink
Professor 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 Deptartment 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 lear
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