GOPred: GO molecular function prediction by combined classifiers.
Functional protein annotation is an important matter for in vivo and in silico biology. Several computational methods have been proposed that make use of a wide range of features such as motifs, domains, homology, structure and physicochemical properties. There is no single method that performs best in all functional classification problems because information obtained using any of these features depends on the function to be assigned to the protein. In this study, we portray a novel approach that combines different methods to better represent protein function. First, we formulated the function annotation problem as a classification problem defined on 300 different Gene Ontology (GO) terms from molecular function aspect. We presented a method to form positive and negative training examples while taking into account the directed acyclic graph (DAG) structure and evidence codes of GO. We applied three different methods and their combinations. Results show that combining different methods improves prediction accuracy in most cases. The proposed method, GOPred, is available as an online computational annotation tool (http://kinaz.fen.bilkent.edu.tr/gopred).
Saraç
Omer Sinan
OS
Department of Computer Engineering, Middle East Technical University, Ankara, Turkey.
Atalay
Volkan
V
Cetin-Atalay
Rengul
R
D019295:Computational Biology / Q000379:methods*; D006801:Humans; D020407:Internet; D011506:Proteins / Q000145:classification* / Q000378:metabolism*
D011506:Proteins
Gene. 2008 May 31;415(1-2):60-7
18403135
Proteins. 2005 Mar 1;58(4):955-70
15645499
Comput Biol Chem. 2008 Apr;32(2):122-30
18243801
Protein Sci. 2006 Jun;15(6):1550-6
16672240
Trends Genet. 2000 Jun;16(6):276-7
10827456
Antioxid Redox Signal. 2004 Feb;6(1):63-74
14713336
Cancer Res. 2006 Feb 1;66(3):1346-53
16452188
Mol Cell Biol. 1986 Feb;6(2):730-4
3537694
Proc Natl Acad Sci U S A. 2008 Apr 8;105(14):5396-401
18385383
Bioinformatics. 2008 Mar 15;24(6):798-806
18263643
Cells Tissues Organs. 2008;188(1-2):91-102
18196925
BMC Bioinformatics. 2004 Nov 18;5:178
15550167
Nucleic Acids Res. 1997 Sep 1;25(17):3389-402
9254694
Math Biosci. 2005 Feb;193(2):223-34
15748731
Yeast. 2000 Dec;17(4):283-93
11119305
Protein Sci. 2006 Jun;15(6):1557-62
16672244
Bioinformatics. 2003;19 Suppl 1:i26-33
12855434
J Comput Biol. 2005;12(1):64-82
15725734
Bioinformatics. 2004 Mar 1;20(4):467-76
14990442
J Biol Chem. 2004 Apr 2;279(14):14256-63
14734546
J Mol Biol. 2002 Jun 21;319(5):1257-65
12079362
PLoS Comput Biol. 2005 Oct;1(5):e45
16217548
BMC Bioinformatics. 2008 Sep 22;9:389
18808707
J Mol Biol. 2000 Oct 13;303(1):61-76
11021970
Nat Rev Cancer. 2008 Feb;8(2):133-40
18219307
Cell. 1992 Aug 21;70(4):523-6
1505019
Proteins. 2001 Aug 15;44(3):361-9
11455609
Nucleic Acids Res. 2005 Jan 1;33(Database issue):D154-9
15608167
Proteins. 2000 Oct 1;41(1):98-107
10944397
BMC Genomics. 2009 May 28;10:250
19476624
Bioinformatics. 2002 Jan;18(1):147-59
11836223
Blood. 1970 Aug;36(2):219-27
5427456
Proc IEEE Comput Syst Bioinform Conf. 2005;:80-5
16447965
Nucleic Acids Res. 2003 Jul 1;31(13):3692-7
12824396
Trends Cell Biol. 2005 Feb;15(2):112-20
15695098
J Mol Biol. 1990 Oct 5;215(3):403-10
2231712
Brief Bioinform. 2006 Sep;7(3):225-42
16772267