[Apologies if you receive this more than once]
Dear Colleague,
We are pleased to share with you a recent review of several hundred papers
in the field of computational protein function prediction:
Title: Computational Approaches for Protein Function Prediction: A Survey
Authors: Gaurav Pandey <http://www.cs.umn.edu/%7Egaurav>, Vipin
Kumar<http://www.cs.umn.edu/%7Ekumar>and Michael
Steinbach <http://www.cs.umn.edu/%7Esteinbac>
Available at: http://www.cs.umn.edu/~kumar/papers/survey.php<http://www.cs.umn.edu/%7Ekumar/papers/survey.php>
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.
Your comments on the article, or any part thereof, are welcome.
Thanks and best regards
Gaurav Pandey (gaurav At cs.umn.edu)
Vipin Kumar (kumar At cs.umn.edu)
Michael Steinbach (steinbac At cs.umn.edu)