A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
IEEE Intelligent Systems
Bioinformatics
Protein structural classification using orthogonal transformation and class-association rules
International Journal of Data Mining and Bioinformatics
Prediction of inter-residue contact clusters from hydrophobic cores
International Journal of Data Mining and Bioinformatics
Accuracy of protein hydropathy predictions
International Journal of Data Mining and Bioinformatics
SVM-RFE based feature selection for tandem mass spectrum quality assessment
International Journal of Data Mining and Bioinformatics
Improving accuracy of microarray classification by a simple multi-task feature selection filter
International Journal of Data Mining and Bioinformatics
Predicting functional residues of protein sequence alignments as a feature selection task
International Journal of Data Mining and Bioinformatics
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To determine the structure of a protein by X-ray crystallography, the protein needs to be purified and crystallized first. However, some proteins cannot be crystallized. This makes the average cost of protein structure determination much higher. Thus it is desired to predict the crystallizability of a protein by a computational method before starting the wet-lab procedure. Features from the primary structure of a target protein are collected first. With a proper set of features, protein crystallizability can be predicted with a high accuracy. In this research, 74 features from previous researches are re-examined by two filter-mode feature selection methods. The selected features are then used for crystallization prediction by three versions of AdaBoost. The Support Vector Machines SVMs are also tested for comparison. The best prediction accuracy of AdaBoost reaches 93 percent and 48 important features are identified from the collected 74 features.