Editing Gene prediction (section)

=== Neural networks ===

[[Artificial neural networks]] are computational models that excel at [[machine learning]] and [[pattern recognition]]. Neural networks must be [[Artificial neural network#Learning|trained]] with example data before being able to generalise for experimental data, and tested against benchmark data. Neural networks are able to come up with approximate solutions to problems that are hard to solve algorithmically, provided there is sufficient training data. When applied to gene prediction, neural networks can be used alongside other ''ab initio'' methods to predict or identify biological features such as splice sites.<ref name="Goel2013">{{cite journal | vauthors = Goel N, Singh S, Aseri TC | title = A comparative analysis of soft computing techniques for gene prediction | journal = Analytical Biochemistry | volume = 438 | issue = 1 | pages = 14–21 | date = July 2013 | pmid = 23529114 | doi = 10.1016/j.ab.2013.03.015 }}</ref> One approach<ref name="Johansen2009">{{cite book|series=Lec Not Comp Sci|volume=5488|year=2009|doi=10.1007/978-3-642-02504-4_9|pages=102–113|last1=Johansen|first1=∅Ystein|last2=Ryen|first2=Tom|last3=Eftes∅l|first3=Trygve|last4=Kjosmoen|first4=Thomas|last5=Ruoff|first5=Peter|title=Computational Intelligence Methods for Bioinformatics and Biostatistics |chapter=Splice Site Prediction Using Artificial Neural Networks |isbn=978-3-642-02503-7}}</ref> involves using a sliding window, which traverses the sequence data in an overlapping manner. The output at each position is a score based on whether the network thinks the window contains a donor splice site or an acceptor splice site. Larger windows offer more accuracy but also require more computational power. A neural network is an example of a signal sensor as its goal is to identify a functional site in the genome.