Computational Approaches for Protein Function Prediction: A Survey [pdf]
Abstract
Proteins are the most essential and versatile macromolecules of life,
and the knowledge of their functions is a crucial link in the
development of new drugs, better crops, and even the development of
synthetic biochemicals such as biofuels. Experimental procedures for
protein function prediction are inherently low throughput and are thus
unable to annotate a non-trivial fraction of proteins that are becoming
available due to rapid advances in genome sequencing technology. This
has motivated the development of computational techniques that utilize
a variety of high-throughput experimental data for protein function
prediction, such as protein and genome sequences, gene expression data,
protein interaction networks and phylogenetic profiles. Indeed, in a
short period of a decade, several hundred articles have been published
on this topic. This review aims to discuss this wide spectrum of
approaches by categorizing them in terms of the data type they use for
predicting function, and thus identify the trends and needs of this
very important field. The survey is expected to be useful for
computational biologists and bioinformaticians aiming to get an
overview of the field of computational function prediction, and
identify areas that can benefit from further research.
Citation
Gaurav
Pandey, Vipin
Kumar and Michael
Steinbach, "Computational
Approaches
for
Protein Function Prediction: A Survey", TR 06-028, Department of
Computer Science
and Engineering, University of Minnesota, Twin Cities [Bibtex]
Upcoming Book!
Gaurav
Pandey, Vipin
Kumar and Michael
Steinbach, "Computational
Approaches
for
Protein Function Prediction", to be published in the Wiley
Book Series On Bioinformatics in Fall 2007 (Expected)