The nature of statistical learning theory
The nature of statistical learning theory
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Prediction of Protein-Protein Interactions Using Support Vector Machines
BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
Kernel methods for predicting protein--protein interactions
Bioinformatics
Domain boundary prediction based on profile domain linker propensity index
Computational Biology and Chemistry
Protein remote homology detection based on binary profiles
BIRD'07 Proceedings of the 1st international conference on Bioinformatics research and development
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
PIMiner: a web tool for extraction of protein interactions from biomedical literature
International Journal of Data Mining and Bioinformatics
A supervised approach to detect protein complex by combining biological and topological properties
International Journal of Data Mining and Bioinformatics
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The prediction of protein protein interactions is a difficult problem in biology. In this study, an efficient method is presented to predict protein protein interactions with sequence composition information. Four kinds of basic building blocks of protein sequences are investigated. The experimental results show that there is minor difference in prediction performance among the four kinds of different building blocks. The method based on combination of all building blocks out performs any of the building blocks. We also demonstrate that the use of Latent Semantic Analysis (LSA) can efficiently remove noise and improve the prediction efficiency without significantly degrading the performance.