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Open Access Research article

A machine learning approach for the identification of odorant binding proteins from sequence-derived properties

Ganesan Pugalenthi1, Ke Tang12, PN Suganthan1, G Archunan3 and R Sowdhamini4*

Author Affiliations

1 School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore

2 Nature Inspired Computation and Applications Laboratory (NICAL), Department of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China

3 Department of Animal Science, Bharathidasan University Trichirapalli, Tamilnadu, 620 024, India

4 National Centre for Biological Sciences, UAS-GKVK campus, Bellary Road, Bangalore 560 065, India

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BMC Bioinformatics 2007, 8:351  doi:10.1186/1471-2105-8-351

Published: 19 September 2007

Additional files

Additional file 1:

Positive training dataset. This data provides 476 protein sequences that are used for training.

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Additional file 2:

Negative training dataset. This data provides 2157 protein sequences that are used for training.

Format: DOC Size: 194KB Download file

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Additional file 3:

Independent testing dataset. This data provides 414 protein sequences that are used for testing.

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Additional file 4:

Prediction results of 414 odorant binding proteins. This table provides prediction results for 414 odorant binding proteins by our method, BLAST and HMM, where "+" represents proteins correctly predicted as odorant binding proteins, and "-" represents proteins incorrectly predicted as non odorant binding proteins.

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