Editing Gene regulatory network (section)

== Prediction ==

Other work has focused on predicting the gene expression levels in a gene regulatory network. The approaches used to model gene regulatory networks have been constrained to be interpretable and, as a result, are generally simplified versions of the network. For example, Boolean networks have been used due to their simplicity and ability to handle noisy data but lose data information by having a binary representation of the genes. Also, artificial neural networks omit using a hidden layer so that they can be interpreted, losing the ability to model higher order correlations in the data. Using a model that is not constrained to be interpretable, a more accurate model can be produced. Being able to predict gene expressions more accurately provides a way to explore how drugs affect a system of genes as well as for finding which genes are interrelated in a process. This has been encouraged by the DREAM competition<ref>{{cite web |title=The DREAM Project |publisher=Columbia University Center for Multiscale Analysis Genomic and Cellular Networks (MAGNet)  |url= http://wiki.c2b2.columbia.edu/dream/index.php/The_DREAM_Project}}</ref> which promotes a competition for the best prediction algorithms.<ref>{{cite journal | vauthors = Gustafsson M, Hörnquist M | title = Gene expression prediction by soft integration and the elastic net-best performance of the DREAM3 gene expression challenge | journal = PLOS ONE | volume = 5 | issue = 2 | pages = e9134 | date = February 2010 | pmid = 20169069 | pmc = 2821917 | doi = 10.1371/journal.pone.0009134 | doi-access = free | bibcode = 2010PLoSO...5.9134G }}</ref> Some other recent work has used artificial neural networks with a hidden layer.<ref>{{cite conference |vauthors=Smith MR, Clement M, Martinez T, Snell Q |title=Time Series Gene Expression Prediction using Neural Networks with Hidden Layers |book-title=Proceedings of the 7th Biotechnology and Bioinformatics Symposium (BIOT 2010) |pages=67–69 |year=2010 |url=http://axon.cs.byu.edu/papers/smith_2010biot.pdf }}</ref>