2007
DOI: 10.1002/pmic.200700422
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Bayesian methods for proteomics

Abstract: Biological and medical data have been growing exponentially over the past several years [1,2]. In particular, proteomics has seen automation dramatically change the rate at which data are generated [3]. Analysis that systemically incorporates prior information is becoming essential to making inferences about the myriad, complex data [4][5][6]. A Bayesian approach can help capture such information and incorporate it seamlessly through a rigorous, probabilistic framework. This paper starts with a review of the b… Show more

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Cited by 19 publications
(11 citation statements)
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“…Causal relations among genes can also be naturally modeled using Bayesian networks which can represent conditional dependencies between expression levels (for a primer on Bayesian network analysis utilizing expression data (see [24]); for a recent review see [25]). Considering the temporal aspects of gene expression profiles, dynamic Bayesian networks have been used to model feedback loops as well as gene regulation patterns [26], [27].…”
Section: Interactomementioning
confidence: 99%
“…Causal relations among genes can also be naturally modeled using Bayesian networks which can represent conditional dependencies between expression levels (for a primer on Bayesian network analysis utilizing expression data (see [24]); for a recent review see [25]). Considering the temporal aspects of gene expression profiles, dynamic Bayesian networks have been used to model feedback loops as well as gene regulation patterns [26], [27].…”
Section: Interactomementioning
confidence: 99%
“…In a Bayesian network model, probabilities define the relationship between the current node and its predecessor or parent in a graph (Alterovitz et al, 2007). Markov models are another network-based technique that can provide a framework to describe molecular or cellular states and the weighted probability of transitioning between them.…”
Section: Computational Techniques and Advances: Systems Biology Applimentioning
confidence: 99%
“…1). It also can help in making sense of raw data, allowing researchers to concentrate on other tasks [1,2]. The need for applying automation in proteomics is increasing daily as larg- Figure 1.…”
Section: Proteomics and Automationmentioning
confidence: 99%
“…For example, innovation among drug companies has progressed to the point where automated methods of targeting and screening have replaced traditional, manual techniques [1]. Moreover, in order to make whole-organism proteomebased experiments a reality, machine learning techniques, such as predicting the functional implications of certain posttranslational alteration, are becoming an indispensable part of the research [1].…”
Section: Proteomics and Automationmentioning
confidence: 99%
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