A Primer on Learning in Bayesian Networks for Computational Biology

By C. Needham, James R. Bradford, A. Bulpitt, David R. Westhead. PLoS Comput. Biol., 2007

305 citations recorded by the source corpus.

Bayesian networks (BNs) provide a neat and compact representation for expressing joint probability distributions (JPDs) and for inference. They are becoming increasingly important in the biological sciences for the tasks of inferring cellular networks [1], modelling protein signalling pathways [2], systems biology, data integration [3], classification [4], and genetic data analysis [5]. The representation and use of probability theory makes BNs suitable for combining domain knowledge and data, expressing causal relationships, avoiding overfitting a model to training data, and learning from incomplete datasets. The probabilistic formalism provides a natural treatment for the stochastic nature of biological systems and measurements. This primer aims to introduce BNs to the computational biologist, focusing on the concepts behind methods for learning the parameters and structure of models, at a time when they are becoming the machine learning method of choice. There are many applications in biology where we wish to classify data; for example, gene function prediction. To solve such problems, a set of rules are required that can be used for prediction, but often such knowledge is unavailable, or in practice there turn out to be many exceptions to the rules or so many rules that this approach produces poor results. Machine learning approaches often produce better results, where a large number of examples (the training set) is used to adapt the parameters of a model that can then be used for performing predictions or classifications on data. There are many different types of models that may be required and many different approaches to training the models, each with its pros and cons. An excellent overview of the topic can be found in [6] and [7]. Neural networks, for example, are often able to learn a model from training data, but it is often difficult to extract information about the model, which with other methods can provide valuable insights into the data or problem being solved. A common problem in machine learning is overfitting, where the learned model is too complex and generalises poorly to unseen data. Increasing the size of the training dataset may reduce this; however, this assumes more training data is readily available, which is often not the case. In addition, often it is important to determine the uncertainty in the learned model parameters or even in the choice of model. This primer focuses on the use of BNs, which offer a solution to these issues. The use of Bayesian probability theory provides mechanisms for describing uncertainty and for adapting the number of parameters to the size of the data. Using a graphical representation provides a simple way to visualise the structure of a model. Inspection of models can provide valuable insights into the properties of the data and allow new models to be produced.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/161db5205859cd30d5d637ebf6d443030d95619e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Molecular Biology

Citations in the corpus

Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling (2014, background): “…al. 2002; Stuart et al. 2003); Boolean networks (Kauffman 1969; Thomas 1973; Bornholdt 2008); Bayesian networks (Heckerman 1996; Imoto et al. 2003; Needham et al. 2007; Werhli and Husmeier 2007); latent variable models (Shojaie and Michailidis 2009); and other regression models (Kim et al. 2009).”
Gene regulatory network inference: Data integration in dynamic models - A review (2009, background): “One way to do this is to use knowledge about which genes code for transcription factors. For instance, one can start to select (known or putative) TFs, which are differentially expressed or just belong to a certain process of interest. Then, further genes can be additionally selected on the basis of”
Geometric Interpretation of Gene Coexpression Network Analysis (2008, unknown): “, [13,55–61].”
Structure learning in action (2010, methodology): “Ultimately, a Bayesian network is a graphical method to efficiently represent the joint distribution of a set of random variables [2,6,8].”