Automatic annotation of protein functional class from sparse and imbalanced data sets

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4316 LNBI, Issue , 2006, Pages 65-77

  Jung, Jaehee ^a   Thon, Michael R ^a  

^a TEXAS A AND M UNIVERSITY   (United States)

Author keywords

Feature selection; Gene annotation; Gene ontology; Imbalanced data; Interpro

Indexed keywords

DATA STRUCTURES; FEATURE EXTRACTION; GENES; ONTOLOGY;

GENE ANNOTATION; GENE ONTOLOGY; IMBALANCED DATA;

PROTEINS;

EID: 34547466817     PISSN: 03029743     EISSN: 16113349     Source Type: Conference Proceeding    
DOI: 10.1007/11960669_7     Document Type: Conference Paper

References (27)

1. Al-shahib, A., Breitling, R. and Gilbert, D. : Feature Selection and the Class Imbalance Problem in Predict Protein Function form sequence. Applied Bioinformatics Vol.4 (2005) 195-203
2. Chawla, N.V., Bowyer, K. and Hall, L.O. : Kegelmeyer,W.P. SMOTE: Synthetic minority over sampling technique. Journal of artificial Intelligence Research, Vol.16 (2002) 321-357
3. Drummond, C. and Holte, R.C. : C4.5,Class Imbalance, and Cost sensitivity: Why Under-sampling beats Oversampling. ICML'2003 Workshop on Learning from Imbalanced Datasets II. (2003)
4. Guyon, I. and Elisseeff, A. : An introduction to variable and feature selection. Journal of Machine Learning Research Vol.3 (2003) 1157-1182
5. Hennig, S., Groth, D. and Lehrach, H. : Automated Gene Ontology annotation for anonymous sequence data. Nucleic acids Research (2003) 3712-3715
6. Huang, J., Lu, J. and Ling, C.X. : Comparing Naive Bayes, Decision Trees, and SVM using Accuracy and AUC. Proc. of The Third IEEE Inter. Conf. on Data Mining (ICDM) (2003) 553-556
7. Japkowics, N. and Stepen, S. : The class imbalanced problem : A systematic study. Intelligent Data Analysis Vol.6 (2002)
8. Khan, S., Situ, G., Decker,K. and Schmidt,C.J. : GoFigure:Automated Gene Ontology annotation. Bioinformatics Vol.19 (2003)
9. King, R.D., Karwath, A.,Clare,A. and Dephaspe,L. : Genome scale prediction of protein functional class from sequence using data mining. Proc. of the sixth ACM SIGKDD Inter. Conf. on Knowledge discovery and data mining (2003)
10. Kubat, M. and Matwin, S. : Addressing the curse of Imbalanced Training sets : One-sided Selection. Proc. of the Fourteenth Inter. Conf. on Machine Learning Proc. (ICML) (1997) 179-186
11. Ling, C. and Li, C. : Data mining for direct marketing :problem and solution. Proc. of the Fourth Inter. Conf. on Knowledges Discovery and Data Mining(KDD) (1998) 73-79
12. Martin, D.M., Berriman, M. and Barton, G.J. : GOtcha : A new method for prediction of protein function assessed by the annotation of sever genomes. BMC bioinformatics Vol.5 (2004)
13. Pavalidis, P., Weston, J., Cai, J. and Grundy, W.B. : Gene Functional Classification From Heterogeneous Data. Proc. of the Fifth Inter. Conf. on Research in Computational Molecular Biology (RECOMB) (2001) 249-255
14. Vinayagam, A., Konig, R., Moormann, J., Schubert, F., Elis, R., Glatting, K.H. and Suhai, S. : Applying support vector machine for gene ontology based gene function prediction. BMC Bioinformatics Vol.19 (2003)
15. Vinayagam, A., Val, CD, Schubert, F., Elis, R., Glatting, K.H., Suhai, S. and Konig, R. : GOPET : A tool for automated predictions of Gene Ontology terms. BMC Bioinformatics Vol.7 (2006)
16. Weiss,G.M. : Mining with rarity : A unifying framework. ACM SIGKDD Explorations Newsletter Vol.6 (2004) 7-19
17. Yang, Y. and Pedersen, J.O. : A comparative study on feature selection in text categorization. Proc. of the Fourteenth Inter. Conf. on Machine Learning (ICML) (1997) 412-420
18. Yu, L. and Liu, H. : Feature Selection for high-Dimensional Data: A Fast Correlation-based filter solution. Proc. of the Twentieth Inter. Conf. on Machine Learning (ICML) (2003)
19. Zehetner, G. : OntoBlast function: from sequence similarities directly to potential functional annotations by ontology terms. Nucleic acids Research (2003) 3799-3803
20. Zhang, J. and Mani, I. : kNN Approach to Unbalanced Data Distributions: A case study involving Information Extraction. ICML'2003 Workshop on learning from imbalanced datasets II (2003)
21. Zheng, Z., Wu, X. and Shrihari, R. : Feature selection for text categorization on imbalanced data. ACM SIGKDD Exploration Newsletter Vol.6 (2004) 80-89
22. Gene Ontology(GO) Consortium, http://www.geneontology.org/
23. InterPro, http://www.ebi.ac.uk/interpro/
24. MATLAB, http://www.mathworks.com/
25. Pattern Recognition Toolbox for MATLAB, http://cmp.felk.cvut.cz/~xfrancv/ stprtool/
26. UniProt, www.uniprot.org/
27. WEKA, http://www.cs.waikato.ac.nz/~ml/