Chengjin Zhang

Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributes

BackgroundAptamer-protein interacting pairs play a variety of physiological functions and therapeutic potentials in organisms. Rapidly and effectively predicting aptamer-protein interacting pairs is significant to design aptamers binding to certain interested proteins, which will give insight into understanding mechanisms of aptamer-protein interacting pairs and developing aptamer-based therapies.ResultsIn this study, an ensemble method is presented to predict aptamer-protein interacting pairs with hybrid features. The features for aptamers are extracted from Pseudo K-tuple Nucleotide Composition (PseKNC) while the features for proteins incorporate Discrete Cosine Transformation (DCT), disorder information, and bi-gram Position Specific Scoring Matrix (PSSM). We investigate predictive capabilities of various feature spaces. The proposed ensemble method obtains the best performance with Youden’s Index of 0.380, using the hybrid feature space of PseKNC, DCT, bi-gram PSSM, and disorder information by 10-fold cross validation. The Relief-Incremental Feature Selection (IFS) method is adopted to obtain the optimal feature set. Based on the optimal feature set, the proposed method achieves a balanced performance with a sensitivity of 0.753 and a specificity of 0.725 on the training dataset, which indicates that this method can solve the imbalanced data problem effectively. To evaluate the prediction performance objectively, an independent testing dataset is used to evaluate the proposed method. Encouragingly, our proposed method performs better than previous study with a sensitivity of 0.738 and a Youden’s Index of 0.451.ConclusionsThese results suggest that the proposed method can be a potential candidate for aptamer-protein interacting pair prediction, which may contribute to finding novel aptamer-protein interacting pairs and understanding the relationship between aptamers and proteins.

Using the SMOTE technique and hybrid features to predict the types of ion channel-targeted conotoxins.

Conotoxins targeting different ion channels play distinct physiological functions and therapeutic potentials in organisms. Accurate identification of types of ion channel-targeted conotoxins will provide significant clues to reveal the physiological mechanism and pharmacological therapeutic potential of conotoxins. In this study, a random forest based predictor called ICTCPred for the types of ion channel-targeted conotoxin prediction is proposed with hybrid features incorporating CTD (Composition, Transition, and Distribution), g-Gap DC (g-Gap Dipeptide Composition), PP (Physicochemical Properties), and SSI (Secondary Structure Information). To deal with the imbalanced benchmark dataset, the SMOTE Technique (Synthetic Minority Over-sampling Technique) is applied. Based on the above-mentioned individual feature spaces, the average accuracy of ICTCPred lies in the range of 0.729-0.886, indicating the discriminative power of these features. In addition, ICTCPred yields the highest average accuracy of 0.895 using the hybrid feature space of CTD, g-Gap DC, PP and SSI. The Relief-IFS (Incremental Feature Selection) method is adopted to further improve the prediction performance of ICTCPred. Based on the training dataset, ICTCPred achieves satisfactory performance with an average accuracy of 0.910. To evaluate the prediction performance objectively, ICTCPred is compared with previous studies on the same independent testing dataset. Encouragingly, our proposed method performs better than previous studies to identify types of ion channel-targeted conotoxins, with the highest sensitivity of 0.919 for Na(+)-targeted conotoxins, the highest sensitivity of 1 for K(+)-targeted conotoxins, and the highest sensitivity of 1 for Ca(2+)-targeted conotoxins. It is anticipated that ICTCPred can be a potential candidate for the ion channel-targeted conotoxin prediction.

Multiobjective Optimization of PID Controller of PMSM

PID controller is used in most of the current-speed closed-loop control of permanent magnet synchronous motors (PMSM) servo system. However, , , and of PID are difficult to tune due to the multiple objectives. In order to obtain the optimal PID parameters, we adopt a NSGA-II to optimize the PID parameters in this paper. According to the practical requirement, several objective functions are defined. NSGA-II can search the optimal parameters according to the objective functions with better robustness. This approach provides a more theoretical basis for the optimization of PID parameters than the aggregation function method. The simulation results indicate that the system is valid, and the NSGA-II can obtain the Pareto front of PID parameters.

Modelling of Lime Kiln Using Subspace Method with New Order Selection Criterion

This paper is taking actual control demand of rotary kiln as background and builds a calcining belt state space model using PO-Moesp subspace method. A novel order-delay double parameters error criterion (ODC) is presented to reduce the modeling order. The proposed subspace order identification method takes into account the influence of order and delay on model error criterion simultaneously. For the introduction of the delay factors, the order is reduced dramatically in the system modeling. Also, in the data processing part sliding-window method is adopted for stripping delay factor from historical data. For this, the parameters can be changed flexibly. Some practical problems in industrial kiln process modeling are also solved. Finally, it is applied to an industrial kiln case.

A Fast Elitism Gaussian Estimation of Distribution Algorithm and Application for PID Optimization

Estimation of distribution algorithm (EDA) is an intelligent optimization algorithm based on the probability statistics theory. A fast elitism Gaussian estimation of distribution algorithm (FEGEDA) is proposed in this paper. The Gaussian probability model is used to model the solution distribution. The parameters of Gaussian come from the statistical information of the best individuals by fast learning rule. A fast learning rule is used to enhance the efficiency of the algorithm, and an elitism strategy is used to maintain the convergent performance. The performances of the algorithm are examined based upon several benchmarks. In the simulations, a one-dimensional benchmark is used to visualize the optimization process and probability model learning process during the evolution, and several two-dimensional and higher dimensional benchmarks are used to testify the performance of FEGEDA. The experimental results indicate the capability of FEGEDA, especially in the higher dimensional problems, and the FEGEDA exhibits a better performance than some other algorithms and EDAs. Finally, FEGEDA is used in PID controller optimization of PMSM and compared with the classical-PID and GA.

Deep Convolutional Neural Network-Based Autonomous Marine Vehicle Maneuver

The automation level of autonomous marine vehicle is limited which is always semi-autonomy and reliant on operator interactions. In order to improve it, an autonomous collision avoidance method is proposed based on the visual technique as human’s visual system. A deep convolutional neural network (Alexnet), with strong visual processing capability, is adopted for encounter pattern recognition. European Ship Simulator is used to generate some encounter scenes and record the corresponding maneuver operation conforming to the COLREGs (International Regulations for Preventing Collisions at Sea) rules as samples. After the training phase, of Alexnet, it can successfully predict the collision avoidance operation according to the input scene image like crewman; moreover, this can provide operation guidance for the automatic navigation, guidance and control system. Some different encounter situations are simulated, and used to testify the validity of the proposed approach.

Incorporating g-gap dipeptide composition and position specific scoring matrix for identifying antioxidant proteins

Oxidative stress can damage major cell components, including protein, DNA, lipid and cell membranes, which may make cells lose function and induce a wide variety of diseases. As an extensive kind of antioxidants in human and animals, antioxidant proteins are essential to eliminate cell damage and aging problems caused by oxidative stress. Accurate identification of antioxidant proteins is a significant step to reveal the inducement and physiological process of certain types of diseases and aging. Furthermore, newly identified antioxidant proteins may provide candidate targets for curing or alleviating diseases and slowing down the aging process. In this study, a random forest-based approach incorporating PSSM (Position Specific Scoring Matrix) and g-gap dipeptide composition is put forward to distinguish antioxidant proteins from non-antioxidant proteins. To further improve the prediction performance, the information gain combined with incremental feature selection is adopted to obtain optimal features. Compared with prior studies in testing dataset, the proposed method shows excellent predictive performance with accuracy of 0.807, MCC of 0.543, AUC of 0.939, respectively. It is indicated that this method may be an alternative perspective predictor for annotating antioxidant proteins.

JPPRED: Prediction of Types of J-Proteins from Imbalanced Data Using an Ensemble Learning Method

Different types of J-proteins perform distinct functions in chaperone processes and diseases development. Accurate identification of types of J-proteins will provide significant clues to reveal the mechanism of J-proteins and contribute to developing drugs for diseases. In this study, an ensemble predictor called JPPRED for J-protein prediction is proposed with hybrid features, including split amino acid composition (SAAC), pseudo amino acid composition (PseAAC), and position specific scoring matrix (PSSM). To deal with the imbalanced benchmark dataset, the synthetic minority oversampling technique (SMOTE) and undersampling technique are applied. The average sensitivity of JPPRED based on above-mentioned individual feature spaces lies in the range of 0.744–0.851, indicating the discriminative power of these features. In addition, JPPRED yields the highest average sensitivity of 0.875 using the hybrid feature spaces of SAAC, PseAAC, and PSSM. Compared to individual base classifiers, JPPRED obtains more balanced and better performance for each type of J-proteins. To evaluate the prediction performance objectively, JPPRED is compared with previous study. Encouragingly, JPPRED obtains balanced performance for each type of J-proteins, which is significantly superior to that of the existing method. It is anticipated that JPPRED can be a potential candidate for J-protein prediction.

Using a Classifier Fusion Strategy to Identify Anti-angiogenic Peptides

Anti-angiogenic peptides perform distinct physiological functions and potential therapies for angiogenesis-related diseases. Accurate identification of anti-angiogenic peptides may provide significant clues to understand the essential angiogenic homeostasis within tissues and develop antineoplastic therapies. In this study, an ensemble predictor is proposed for anti-angiogenic peptide prediction by fusing an individual classifier with the best sensitivity and another individual one with the best specificity. We investigate predictive capabilities of various feature spaces with respect to the corresponding optimal individual classifiers and ensemble classifiers. The accuracy and Matthew’s Correlation Coefficient (MCC) of the ensemble classifier trained by Bi-profile Bayes (BpB) features are 0.822 and 0.649, respectively, which represents the highest prediction results among the investigated prediction models. Discriminative features are obtained from BpB using the Relief algorithm followed by the Incremental Feature Selection (IFS) method. The sensitivity, specificity, accuracy, and MCC of the ensemble classifier trained by the discriminative features reach up to 0.776, 0.888, 0.832, and 0.668, respectively. Experimental results indicate that the proposed method is far superior to the previous study for anti-angiogenic peptide prediction.

Information-Fusion Methods Based Simultaneous Localization and Mapping for Robot Adapting to Search and Rescue Postdisaster Environments

The first application of utilizing unique information-fusion SLAM (IF-SLAM) methods is developed for mobile robots performing simultaneous localization and mapping (SLAM) adapting to search and rescue (SAR) environments in this paper. Several fusion approaches, parallel measurements filtering, exploration trajectories fusing, and combination sensors’ measurements and mobile robots’ trajectories, are proposed. The novel integration particle filter (IPF) and optimal improved EKF (IEKF) algorithms are derived for information-fusion systems to perform SLAM task in SAR scenarios. The information-fusion architecture consists of multirobots and multisensors (MAM); multiple robots mount on-board laser range finder (LRF) sensors, localization sonars, gyro odometry, Kinect-sensor, RGB-D camera, and other proprioceptive sensors. This information-fusion SLAM (IF-SLAM) is compared with conventional methods, which indicates that fusion trajectory is more consistent with estimated trajectories and real observation trajectories. The simulations and experiments of SLAM process are conducted in both cluttered indoor environment and outdoor collapsed unstructured scenario, and experimental results validate the effectiveness of the proposed information-fusion methods in improving SLAM performances adapting to SAR scenarios.