Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
A Protein Interaction Verification System Based on a Neural Network Algorithm
CSBW '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference - Workshops
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Enhanced classification for high-throughput data with an optimal projection and hybrid classifier
International Journal of Data Mining and Bioinformatics
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A comparative study for assessing the reliability of protein-protein interactions in a high-throughput dataset is presented. We use various state-of-the-art classification algorithms to distinguish true interacting protein pairs from noisy data using the empirical knowledge about interacting proteins. Then we compare the performance of classifiers with various criteria. Experimental results show that classification algorithms provide very powerful tools in distinguishing true interacting protein pairs from noisy protein-protein interaction dataset. Furthermore, in the data setting with lots of missing values like protein-protein interaction dataset, K-Nearest Neighborhood and Decision Tree algorithms show best performance among other methods.