scholarly journals Reverse engineering of genetic networks with Bayesian networks

2003 ◽  
Vol 31 (6) ◽  
pp. 1516-1518 ◽  
Author(s):  
D. Husmeier
Keyword(s):  
Gene Expression ◽  
Reverse Engineering ◽  
Bayesian Networks ◽  
Gene Expression Data ◽  
Bayesian Learning ◽  
Genetic Networks ◽  
Biochemical Networks ◽  
Receiver Operator Characteristic ◽  
Expression Data ◽  
Learning Paradigm

This paper provides a brief introduction to learning Bayesian networks from gene-expression data. The method is contrasted with other approaches to the reverse engineering of biochemical networks, and the Bayesian learning paradigm is briefly described. The article demonstrates an application to a simple synthetic toy problem and evaluates the inference performance in terms of ROC (receiver operator characteristic) curves.

scholarly journals Network Completion for Static Gene Expression Data

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Natsu Nakajima ◽  
Tatsuya Akutsu
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Synthetic Data ◽  
Optimal Solution ◽  
Genetic Networks ◽  
Expression Data ◽  
Initial Network ◽  
Static Data ◽  
Normal Gene ◽  
Stationary Conditions

We tackle the problem of completing and inferring genetic networks under stationary conditions from static data, where network completion is to make the minimum amount of modifications to an initial network so that the completed network is most consistent with the expression data in which addition of edges and deletion of edges are basic modification operations. For this problem, we present a new method for network completion using dynamic programming and least-squares fitting. This method can find an optimal solution in polynomial time if the maximum indegree of the network is bounded by a constant. We evaluate the effectiveness of our method through computational experiments using synthetic data. Furthermore, we demonstrate that our proposed method can distinguish the differences between two types of genetic networks under stationary conditions from lung cancer and normal gene expression data.

Reverse-Engineering Transcriptional Modules from Gene Expression Data

2009 ◽  
Vol 1158 (1) ◽  
pp. 36-43 ◽  
Author(s):  
Tom Michoel ◽  
Riet De Smet ◽  
Anagha Joshi ◽  
Kathleen Marchal ◽  
Yves Van de Peer
Keyword(s):  
Gene Expression ◽  
Reverse Engineering ◽  
Gene Expression Data ◽  
Expression Data

scholarly journals Learning Parsimonious Classification Rules from Gene Expression Data Using Bayesian Networks with Local Structure

2016 ◽  
Author(s):  
Jonathan Lyle Lustgarten ◽  
Jeya Balaji Balasubramanian ◽  
Shyam Visweswaran ◽  
Vanathi Gopalakrishnan
Keyword(s):  
Gene Expression ◽  
Decision Tree ◽  
Bayesian Networks ◽  
Gene Expression Data ◽  
Local Structure ◽  
Biomarker Discovery ◽  
Predictive Performance ◽  
Global Constraints ◽  
Expression Data ◽  
Worst Case

The comprehensibility of good predictive models learned from high-dimensional gene expression data is attractive because it can lead to biomarker discovery. Several good classifiers provide comparable predictive performance but differ in their abilities to summarize the observed data. We extend a Bayesian Rule Learning (BRL-GSS) algorithm, previously shown to be a significantly better predictor than other classical approaches in this domain. It searches a space of Bayesian networks using a decision tree representation of its parameters with global constraints, and infers a set of IF-THEN rules. The number of parameters and therefore the number of rules are combinatorial to the number of predictor variables in the model. We relax these global constraints to a more generalizable local structure (BRL-LSS). BRL-LSS entails more parsimonious set of rules because it does not have to generate all combinatorial rules. The search space of local structures is much richer than the space of global structures. We design the BRL-LSS with the same worst-case time-complexity as BRL-GSS while exploring a richer and more complex model space. We measure predictive performance using Area Under the ROC curve (AUC) and Accuracy. We measure model parsimony performance by noting the average number of rules and variables needed to describe the observed data. We evaluate the predictive and parsimony performance of BRL-GSS, BRL-LSS and the state-of-the-art C4.5 decision tree algorithm, across 10-fold cross-validation using ten microarray gene-expression diagnostic datasets. In these experiments, we observe that BRL-LSS is similar to BRL-GSS in terms of predictive performance, while generating a much more parsimonious set of rules to explain the same observed data. BRL-LSS also needs fewer variables than C4.5 to explain the data with similar predictive performance. We also conduct a feasibility study to demonstrate the general applicability of our BRL methods on the newer RNA sequencing gene-expression data.

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