BioCAD: an information fusion platform for bio-network inference and analysis
TMBIO '06 Proceedings of the 1st international workshop on Text mining in bioinformatics
Towards scalable and data efficient learning of Markov boundaries
International Journal of Approximate Reasoning
Inference of a probabilistic Boolean network from a single observed temporal sequence
EURASIP Journal on Bioinformatics and Systems Biology
A hybrid Bayesian network learning method for constructing gene networks
Computational Biology and Chemistry
Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers
Computer Methods and Programs in Biomedicine
Learning Bayesian network parameters under incomplete data with domain knowledge
Pattern Recognition
Graph-Based Analysis of Nasopharyngeal Carcinoma with Bayesian Network Learning Methods
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Exploiting Data Missingness in Bayesian Network Modeling
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Bounding the false discovery rate in local Bayesian network learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
The Journal of Machine Learning Research
Causal inference of regulator-target pairs by gene mapping of expression phenotypes
RECOMB'05 Proceedings of the 2005 joint annual satellite conference on Systems biology and regulatory genomics
Learning Gaussian graphical models of gene networks with false discovery rate control
EvoBIO'08 Proceedings of the 6th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Analysis of nasopharyngeal carcinoma risk factors with Bayesian networks
Artificial Intelligence in Medicine
Ensemble transcript interaction networks: A case study on Alzheimer's disease
Computer Methods and Programs in Biomedicine
Score-based methods for learning Markov boundaries by searching in constrained spaces
Data Mining and Knowledge Discovery
Learning the local Bayesian network structure around the ZNF217 oncogene in breast tumours
Computers in Biology and Medicine
Growing Seed Genes from Time Series Data and Thresholded Boolean Networks with Perturbation
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Motivation: For the last few years, Bayesian networks (BNs) have received increasing attention from the computational biology community as models of gene networks, though learning them from gene-expression data is problematic. Most gene-expression databases contain measurements for thousands of genes, but the existing algorithms for learning BNs from data do not scale to such high-dimensional databases. This means that the user has to decide in advance which genes are included in the learning process, typically no more than a few hundreds, and which genes are excluded from it. This is not a trivial decision. We propose an alternative approach to overcome this problem. Results: We propose a new algorithm for learning BN models of gene networks from gene-expression data. Our algorithm receives a seed gene S and a positive integer R from the user, and returns a BN for the genes that depend on S such that less than R other genes mediate the dependency. Our algorithm grows the BN, which initially only contains S, by repeating the following step R + 1 times and, then, pruning some genes; find the parents and children of all the genes in the BN and add them to it. Intuitively, our algorithm provides the user with a window of radius R around S to look at the BN model of a gene network without having to exclude any gene in advance. We prove that our algorithm is correct under the faithfulness assumption. We evaluate our algorithm on simulated and biological data (Rosetta compendium) with satisfactory results. Contact: jmp@ifm.liu.se