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Invited: Prediction of protein function

Published: 10 April 2003 Publication History

Abstract

A genome sequence embodies the potential life of an organism, but implementation of genetic information depends on the functions of the proteins that it encodes. Many proteins of known sequence and even of known structure present challenges to understanding their function. In some cases genes responsible for diseases have been identified but their specific functions are unknown. Annotation of a genome involves assignment of functions to gene products, in most cases on the basis of amino acid sequence alone, in the absence of experimental information. Protein structures from structural genomics projects are invaluable for function assignment. Nevertheless, prediction of protein function remains a difficult problem. Some methods provide reasonable guesses, but no method is foolproof. Moreover, even when it is possible to ascribe a particular function to a gene product, the protein may have multiple functions. An underlying problem has been that function is in many cases an ill-defined concept.

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cover image ACM Conferences
RECOMB '03: Proceedings of the seventh annual international conference on Research in computational molecular biology
April 2003
352 pages
ISBN:1581136358
DOI:10.1145/640075
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 10 April 2003

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RECOMB '03 Paper Acceptance Rate 35 of 175 submissions, 20%;
Overall Acceptance Rate 148 of 538 submissions, 28%

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