Negative Example Selection for Protein Function Prediction: The NoGO Database

PLoS Computational Biology

Volumn 10, Issue 6, 2014, Pages

  Youngs, Noah ^a   Penfold Brown, Duncan ^b   Bonneau, Richard ^a,b   Shasha, Dennis ^a,b  

^a POLYTECHNIC UNIVERSITY   (United States)

^b NEW YORK UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); FORECASTING; GENE ONTOLOGY; GENES; LEARNING SYSTEMS; PROTEINS; TEXT PROCESSING;

'CURRENT; EXAMPLE SELECTION; GENE ANNOTATION; GENE ONTOLOGY; MACHINE LEARNING METHODS; NEGATIVE EXAMPLES; NON-TRIVIAL TASKS; OPEN WORLD ASSUMPTION; PROTEIN FUNCTION PREDICTION; PROTEIN FUNCTIONS;

DATABASE SYSTEMS;

PROTEOME; RNA; ARABIDOPSIS PROTEIN; PROTEIN; SACCHAROMYCES CEREVISIAE PROTEIN;

ARABIDOPSIS THALIANA; ARTICLE; CLASSIFICATION ALGORITHM; GENETIC ANALYSIS; HUMAN; MOLECULAR BIOLOGY; MOUSE; NOGO DATABASE; NONHUMAN; PREDICTION; PROBABILITY; PROTEIN ANALYSIS; PROTEIN DATABASE; PROTEIN FUNCTION; QUALITY CONTROL; RNA BINDING; SACCHAROMYCES CEREVISIAE; ALGORITHM; ANIMAL; ARTIFICIAL INTELLIGENCE; BIOLOGY; GENE ONTOLOGY; GENETIC DATABASE; GENETICS; GENOME; MOLECULAR GENETICS; PHYSIOLOGY;

ALGORITHMS; ANIMALS; ARABIDOPSIS PROTEINS; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; DATABASES, GENETIC; GENE ONTOLOGY; GENOME; HUMANS; MICE; MOLECULAR SEQUENCE ANNOTATION; PROTEINS; PROTEOME; SACCHAROMYCES CEREVISIAE PROTEINS;

EID: 84903398946     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1003644     Document Type: Article

References (23)

1. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, et al. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25: 25-29.
2. Yousef M, Jung S, Showe LC, Showe MK, (2008) Learning from positive examples when the negative class is undetermined-microRNA gene identification. Algorithms Mol Biol 3: 2.
3. Elkan C, Noto K (2008) Learning classifiers from only positive and unlabeled data. Proc. ACM SIGKDD, pp. 213-220.
4. Liu B, Dai Y, Li X, Lee WS, Yu PS (2003) Building text classifiers using positive and unlabeled examples. IEEE.pp. 179-186.
5. Youngs N, Penfold-Brown D, Drew K, Shasha D, Bonneau R, (2013) Parametric Bayesian priors and better choice of negative examples improve protein function prediction. Bioinformatics 29: 1190-1198.
6. Bhardwaj N, Gerstein M, Lu H, (2010) Genome-wide sequence-based prediction of peripheral proteins using a novel semi-supervised learning technique. BMC Bioinformatics 11Suppl 1: S6.
7. Zhao XM, Wang Y, Chen L, Aihara K, (2008) Gene function prediction using labeled and unlabeled data. BMC Bioinformatics 9: 57.
8. Guan Y, Myers CL, Hess DC, Barutcuoglu Z, Caudy AA, et al. (2008) Predicting gene function in a hierarchical context with an ensemble of classifiers. Genome Biol 9Suppl 1: S3.
9. Gomez SM, Noble WS, Rzhetsky A, (2003) Learning to predict protein-protein interactions from protein sequences. Bioinformatics 19: 1875-1881.
10. Mostafavi S, Morris Q, Using the Gene Ontology hierarchy when predicting gene function; 2009. AUAI Press. pp pp. 419-427.
11. Cesa-Bianchi N, Valentini G (2010) Hierarchical cost-sensitive algorithms for genome-wide gene function prediction. J. Machine Learning Research, W&C Proc.8: Machine Learning in Systems Biology: 14-29.
12. Rocchio JJ (1971) Relevance feedback in information retrieval. In: Salton G, editor. The smart retrieval system- experiments in automatic document processing. Englewood Cliffs: Prentice-Hall, Inc. pp. 313-323.
13. Yu H, Han J, Chang K C-C. PEBL: positive example based learning for Web page classification using SVM; 2002. ACM. pp. 239-248.
14. Puelma T, Gutierrez RA, Soto A, (2012) Discriminative local subspaces in gene expression data for effective gene function prediction. Bioinformatics 28: 2256-2264.
15. Greene CS, Troyanskaya OG, (2012) Accurate evaluation and analysis of functional genomics data and methods. Ann N Y Acad Sci 1260: 95-100.
16. Baltz AG, Munschauer M, Schwanhausser B, Vasile A, Murakawa Y, et al. (2012) The mRNA-bound proteome and its global occupancy profile on protein-coding transcripts. Mol Cell 46: 674-690.
17. Huttenhower C, Hibbs M A, Myers C L, Caudy A A, Hess D C, et al. (2009) The impact of incomplete knowledge on evaluation: an experimental benchmark for protein function prediction. Bioinformatics 25: 2404-2410.
18. Blei DM, Ng AY, Jordan MI, (2003) Latent Dirichlet allocation. Journal of Machine Learning Research 3: 993-1022.
19. Pandey G, Myers CL, Kumar V, (2009) Incorporating functional inter-relationships into protein function prediction algorithms. BMC Bioinformatics 10: 142.
20. King OD, Foulger RE, Dwight SS, White JV, Roth FP, (2003) Predicting gene function from patterns of annotation. Genome Res 13: 896-904.
21. Smialowski P, Pagel P, Wong P, Brauner B, Dunger I, et al. (2010) The Negatome database: a reference set of non-interacting protein pairs. Nucleic Acids Research 38: D540-D544.
22. Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, et al. (2010) The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res 38: W214-220.
23. Jones P, Binns D, Chang HY, Fraser M, Li W, et al. (2014) InterProScan 5: genome-scale protein function classification. Bioinformatics 30(9): 1236-40.