Determining the target genes that interact with drugs—drug–target interactions—plays an important role in drug discovery. Identification of drug–target interactions through biological experiments is time consuming, laborious, and costly. Therefore, using computational approaches to predict candidate targets is a good way to reduce the cost of wet-lab experiments. However, the known interactions (positive samples) and the unknown interactions (negative samples) display a serious class imbalance, which has an adverse effect on the accuracy of the prediction results. To mitigate the impact of class imbalance and completely exploit the negative samples, we proposed a new method, named DTIGBDT, based on gradient boosting decision trees, for predicting candidate drug–target interactions. We constructed a drug–target heterogeneous network that contains the drug similarities based on the chemical structures of drugs, the target similarities based on target sequences, and the known drug–target interactions. The topological information of the network was captured by random walks to update the similarities between drugs or targets. The paths between drugs and targets could be divided into multiple categories, and the features of each category of paths were extracted. We constructed a prediction model based on gradient boosting decision trees. The model establishes multiple decision trees with the extracted features and obtains the interaction scores between drugs and targets. DTIGBDT is a method of ensemble learning, and it effectively reduces the impact of class imbalance. The experimental results indicate that DTIGBDT outperforms several state-of-the-art methods for drug–target interaction prediction. In addition, case studies on Quetiapine, Clozapine, Olanzapine, Aripiprazole , and Ziprasidone demonstrate the ability of DTIGBDT to discover potential drug–target interactions.
Identification of disease-related microRNAs (disease miRNAs) is helpful for understanding and exploring the etiology and pathogenesis of diseases. Most of recent methods predict disease miRNAs by integrating the similarities and associations of miRNAs and diseases. However, these methods fail to learn the deep features of the miRNA similarities, the disease similarities, and the miRNA–disease associations. We propose a dual convolutional neural network-based method for predicting candidate disease miRNAs and refer to it as CNNDMP. CNNDMP not only exploits the similarities and associations of miRNAs and diseases, but also captures the topology structures of the miRNA and disease networks. An embedding layer is constructed by combining the biological premises about the miRNA–disease associations. A new framework based on the dual convolutional neural network is presented for extracting the deep feature representation of associations. The left part of the framework focuses on integrating the original similarities and associations of miRNAs and diseases. The novel miRNA and disease similarities which contain the topology structures are obtained by random walks on the miRNA and disease networks, and their deep features are learned by the right part of the framework. CNNDMP achieves the superior prediction performance than several state-of-the-art methods during the cross-validation process. Case studies on breast cancer, colorectal cancer and lung cancer further demonstrate CNNDMP’s powerful ability of discovering potential disease miRNAs.
In this paper, the line of sight (LOS) guidance law is improved to implement tracking toward a moving target. In the presence of sensor noise, an optimal information fusion Kalman filter weighted by scalars is utilized for two-sensor information fusing, improving the trajectory tracking precision. Under the communication delay, n-step ahead Kalman predictor compensates for communication delay and provides LOS guidance law with more accurate target estimates. The results of the simulation demonstrate the feasibility and effectiveness of the proposed control strategy.
This paper presents a method for pursuer to track a moving target in a three-dimensional space. The method is based on the guidance laws combined with the kinematics equations of the pursuer and the target. The maneuvers of the target are prior unknown to the pursuer. Guidance laws used for tracking are the deviated pursuit and the proportional navigation, and the method presents a family of navigation laws resulting in a rich behavior for different parameters. For the three-dimensional scenario, two cases-not presenting interference and presenting interference are considered. In the absence of interference, the control strategy is proposed to implement the problem of tracking. In the presence of interference, an optimal information fusion Kalman filter weighted by scalars and guidance laws are combined to improve the trajectory tracking precision, and the combination can enrich the application range of information fusion and guidance laws. Simulations are conducted to demonstrate the effectiveness and reliability of the proposed control strategy.
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