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Using triangulation to include target structure improves drug resistance prediction accuracy.

, , and . ICCABS, page 1. IEEE Computer Society, (2013)

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Clustering Support Vector Machines and Its Application to Local Protein Tertiary Structure Prediction., , , , and . International Conference on Computational Science (2), volume 3992 of Lecture Notes in Computer Science, page 710-717. Springer, (2006)Sparse Representation for HIV-1 Protease Drug Resistance Prediction., , and . SDM, page 342-349. SIAM, (2013)Statistical Estimate for the Size of the Protein Structural Vocabulary., , , and . ISBRA, volume 4463 of Lecture Notes in Computer Science, page 530-538. Springer, (2007)Understanding Protein Structure Prediction Using SVM_DT., , , , , and . ISPA Workshops, volume 3759 of Lecture Notes in Computer Science, page 203-212. Springer, (2005)Optimization of relational database usage involving Big Data a model architecture for Big Data applications., , and . CIDM, page 454-462. IEEE, (2014)Clustering support vector machines for protein local structure prediction., , , , and . Expert Syst. Appl., 32 (2): 518-526 (2007)Automated prediction of HIV drug resistance from genotype data., , , and . BMC Bioinform., 17 (S-8): 278 (2016)Understandable learning machine system design for Transmembrane or Embedded Membrane segments prediction., , , and . Int. J. Data Min. Bioinform., 5 (1): 38-51 (2011)Identifying representative drug resistant mutants of HIV., , and . BMC Bioinform., 16 (S17): S1 (December 2015)Protein secondary structure prediction using support vector machine with advanced encoding schemes., , , and . Data Mining and Knowledge Discovery: Theory, Tools, and Technology, volume 5433 of SPIE Proceedings, page 80-87. SPIE, (2004)