Bayesian methods in bioinformatics and computational systems biology.
Bayesian methods are valuable, inter alia, whenever there is a need to extract information from data that are uncertain or subject to any kind of error or noise (including measurement error and experimental error, as well as noise or random variation intrinsic to the process of interest). Bayesian methods offer a number of advantages over more conventional statistical techniques that make them particularly appropriate for complex data. It is therefore no surprise that Bayesian methods are becoming more widely used in the fields of genetics, genomics, bioinformatics and computational systems biology, where making sense of complex noisy data is the norm. This review provides an introduction to the growing literature in this area, with particular emphasis on recent developments in Bayesian bioinformatics relevant to computational systems biology.
Wilkinson
Darren J
DJ
School of Mathematics and Statistics, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK. d.j.wilkinson@ncl.ac.uk
D001185:Artificial Intelligence; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D020543:Proteome / Q000378:metabolism; D017421:Sequence Analysis / Q000379:methods*; D015398:Signal Transduction / Q000502:physiology*; D049490:Systems Biology / Q000379:methods
D020543:Proteome