Browse reports by year:
[ ALL 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 ]
Browse report authors:
[ ALL A B C D E F G H I J K L M N O P Q R S T U V W X Y Z ]
Browse reports by title:
[ ALL A B C D E F G H I J K L M N O P Q R S T U V W X Y Z ]

University of Minnesota - Computer Science and Engineering Technical Report Abstract

Computational Approaches for Protein Function Prediction: A Survey

Report Number: 06-028
Date of Submission: 10/31/2006

Authors:
   
   
   

View Report:
   PDF format

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 survey 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.