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

Data ◽  
2017 ◽  
Vol 2 (1) ◽  
pp. 5 ◽  
Author(s):  
Jonathan Lustgarten ◽  
Jeya Balasubramanian ◽  
Shyam Visweswaran ◽  
Vanathi Gopalakrishnan
Keyword(s):  
Gene Expression ◽  
Bayesian Networks ◽  
Gene Expression Data ◽  
Local Structure ◽  
Expression Data ◽  
Classification Rules

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.

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.

LEARNING COMPREHENSIBLE CLASSIFICATION RULES FROM GENE EXPRESSION DATA USING GENETIC PROGRAMMING AND BIOLOGICAL ONTOLOGIES

2006 ◽  
Author(s):  
BEN GOERTZEL ◽  
LÚCIO DE SOUZA COELHO ◽  
CASSIO PENNACHIN ◽  
IZABELA FREIRE GOERTZEL ◽  
MURILO SARAIVA DE QUEIROZ ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Programming ◽  
Gene Expression Data ◽  
Expression Data ◽  
Classification Rules ◽  
Biological Ontologies

scholarly journals Combining gene expression data and prior knowledge for inferring gene regulatory networks via Bayesian networks using structural restrictions

2019 ◽  
Vol 18 (3) ◽  
Author(s):  
Luis M. de Campos ◽  
Andrés Cano ◽  
Javier G. Castellano ◽  
Serafín Moral
Keyword(s):  
Gene Expression ◽  
Bayesian Networks ◽  
Prior Knowledge ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Cellular Systems ◽  
Expression Data ◽  
Starting Point ◽  
Gene Regulatory

Abstract Gene Regulatory Networks (GRNs) are known as the most adequate instrument to provide a clear insight and understanding of the cellular systems. One of the most successful techniques to reconstruct GRNs using gene expression data is Bayesian networks (BN) which have proven to be an ideal approach for heterogeneous data integration in the learning process. Nevertheless, the incorporation of prior knowledge has been achieved by using prior beliefs or by using networks as a starting point in the search process. In this work, the utilization of different kinds of structural restrictions within algorithms for learning BNs from gene expression data is considered. These restrictions will codify prior knowledge, in such a way that a BN should satisfy them. Therefore, one aim of this work is to make a detailed review on the use of prior knowledge and gene expression data to inferring GRNs from BNs, but the major purpose in this paper is to research whether the structural learning algorithms for BNs from expression data can achieve better outcomes exploiting this prior knowledge with the use of structural restrictions. In the experimental study, it is shown that this new way to incorporate prior knowledge leads us to achieve better reverse-engineered networks.

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