Oxidoreductase is an enzyme that widely exists in organisms. It plays an important role in cellular energy metabolism and biotransformation processes. Oxidoreductases have many subclasses with different functions, creating an important classification task in bioinformatics. In this paper, a dataset of 2640 oxidoreductase sequences was used to perform an analysis and comparison. The idea of dipeptides was introduced to process the Position Specific Score Matrix (PSSM), since each dipeptide consists of two amino acids and each column of PSSM corresponds to the information of one amino acid. Two kinds of dipeptide scores were proposed, the Standardization Normal Distribution PSSM (SND-PSSM) and the Correlation Coefficient PSSM (CC-PSSM). Recursive Feature Elimination (RFE) is used to extract features from the SND-PSSM and CC-PSSM, and the two sets of extracted features are combined to form a new feature matrix, the RFE-SND-CC-PSSM. The results show that, with the proposed method and a kernel-based nonlinear SVM classifier, the accuracy can reach 95.56% by the Jackknife test. Our method greatly improves the accuracy of oxidoreductase subclass prediction. Using this method to predict the categories of the 6 major types of enzymes effectively improves its prediction accuracy to 94.54%, indicating that this method has general applicability to other protein problems. The results show that our method is effective and universally applicable, and might be complementary to the existing methods.
The problem of trajectory optimization of an unmanned aerial vehicle (UAV) for static target localization with biased bearing measurements is considered. The angular bias in sensor measurements is modeled as an additive constant in the observation model and jointly estimated with the position of the target. The necessary conditions for system observability of this estimation problem is first derived analytically with geometrical interpretations provided. The trajectory of UAV is designed based on the Fisher Information Matrix (FIM) considering physical constraints to enhance the system observability. Simulation results with Monte-Carlo runs are presented to demonstrate the improvement in target localization with biased measurements by UAV trajectory optimization.
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