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Discovering DTI and DDI by Knowledge Graph with MHRW and Improved Neural Network

Discovering DTI and DDI by Knowledge Graph with MHRW and Imp...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Zhang, Shuo Lin, Xiaoli Zhang, Xiaolong Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Computer Science and Technology Wuhan China

ISBN: (纸本)9781665401265

Drug discovery is of great significance in medical and biological research, while the study of Drug-Target Interaction (DTI) and Drug-Drug Interaction (DDI) can help accelerate drug discovery progress. This paper proposes a new hybrid method for DTI prediction and DDI prediction, which is called MHRW2Vec-TBAN that combines graph representation learning and neural network. MHRW2VecTBAN first constructs knowledge graph KG-DTI and KG-DDI that integrate data related to drugs and targets. Then, an improved graph representation learning model, MHRW2Vec model, is used to obtain feature vectors of reflecting the network structure information for improving the performance of representation learning. Finally, the feature vectors obtained are input to the improved neural network model TextCNN-BiLSTM-Attention Network (TBAN). The experimental results show that, compared with other existing methods, our method could discover more deeper the relationship between drugs and their potential neighborhoods, and has great improvements in DTI prediction and DDI prediction. In addition, the case study of prediction COVID-19 DTI also shows that the proposed model has the potential for actual drug discovery. © 2021 IEEE.

关键词： Knowledge graph

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Drug-Target Interaction Prediction via Multiple Output Graph Convolutional Networks 17th

Drug-Target Interaction Prediction via Multiple Output Graph...

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17th International Conference on intelligent Computing, ICIC 2021

作者： Ye, Qing Zhang, Xiaolong Lin, Xiaoli Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783030845315

Computational prediction of drug-target interaction (DTI) is very important for the new drug discovery. Currently, graph convolutional networks (GCNs) have been gained a lot of momentum, as its performance on non-Euclidean data. Although drugs and targets are two typical non-Euclidean data, many problems are also existed when use GCNs in DTI prediction. Firstly, most state-of-the-art GCN models are prone to the vanishing gradient problem, which is more serious in DTI prediction, as the number of interactions is limit. Secondly, a suitable graph is hardly defined for GCN in DTI prediction, as the relationship between the samples is not explicitly provided. To overcome the above problems, in this paper, a multiple output graph convolutional network (MOGCN) based DTI prediction method is designed. MOGCN enhances its learning ability with many new designed auxiliary classifier layers and a new designed graph calculation method. Many auxiliary classifier layers can increase the gradient signal that gets propagated back, utilize multi-level features to train the model, and use the features produced by the higher, middle or lower layers in a unified framework. The graph calculation method can use both the label information and the manifold. The conducted experiments validate the effectiveness of our MOGCN. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

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Budget Constraint Task Allocation for Mobile Crowd Sensing with Hybrid Participant 16th

Budget Constraint Task Allocation for Mobile Crowd Sensing w...

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16th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Wang, Xin Li, Peng Xiao, Junlei College of Computer Science and Technology Wuhan University of Science and Technology Hubei China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei China

ISBN: (纸本)9783030675394

In the Mobile crowdsensing (MCS) system, the task allocation problem is a crucial problem. In this paper, we focus on the task allocation problem for hybrid participants with a budget-constrained MCS system. There are two types of participants: mobile participants and static participants. Mobile participants have low cost, large numbers, and flexibility. However, most of the sensing data they submitted are of low quality. On the other hand, static participants, such as city cameras, roadside infrastructure, have high-quality sensing data. Despite the benefit of high quality, static participants have less coverage and high cost. Given a budget, the problem is how to assign the task to the two types of participants, such that the social welfare is maximized. To solve the problem, we propose a reverse auction-based task allocation method (ORA) to select winning bids round by round. Then, a Shapley value based online algorithm (OAA) is proposed to ensure the task is finished. Moreover, we consider the different types of participants to have a different probability to finish tasks. We exploit the semi-Markov model to calculate the probability that participants finished tasks. We prove that the proposed task allocation method has truthfulness and individual rationality. We conduct extensive experiments to evaluate the performance of our system, and the evaluation results demonstrate the remarkable effect of the proposed task allocation method. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Budget control

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De Novo Drug Design via Multi-Label Learning and Adversarial Autoencoder

De Novo Drug Design via Multi-Label Learning and Adversarial...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Ye, Qing Zhang, Xiaolong Lin, Xiaoli Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology Wuhan China

ISBN: (纸本)9781665401265

generating new molecules is very important for drug design. Currently, many deep generative models have been designed, such as variational autoencoder (VAE), adversarial autoencoder (AAE), and reinforcement learning (RL). However, many problems are also existed in these models. Firstly, the information among molecules could be not utilized in optimizing these models. Secondly, some useful molecule information could be not used, such as fingerprint. Thirdly, the information contained in different molecule representations could be not used together. To overcome the above problems, in this paper, a multi-label learning and adversarial autoencoder (MLAAE) based de novo drug design method is designed. MLAAE enhances its learning ability with a new designed multi-label classifier and a new designed double AAEs collaborative optimization framework. These new designs can learn a better latent space whose global distribution is similar with the random distribution, but local distribution contains much information. As a result, the generator can be trained by more information, and the input of generator in testing is similar with that in training. The conducted experiments validate the effectiveness of our MLAAE. © 2021 IEEE.

关键词： Molecules

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HCU-Net: An Efficient Fully Convolutional Neural Network for Thyroid Nodules Ultrasound Image Segmentation

HCU-Net: An Efficient Fully Convolutional Neural Network for...

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IEEE International Conference on systems, Man and Cybernetics

作者： Mengxi Ren Jun Liu School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Thyroid nodules, which can manifest as solitary or multiple growths, are among the most prevalent diseases with a high incidence rate. When examining thyroid nodules, ultrasound is typically the first step. Ultrasound image segmentation of thyroid nodules can assist physicians in diagnosis. However, the following three factors limit the development of thyroid nodule segmentation: (1) most of the existing studies optimize the segmentation of single nodules, and the incidence of multiple nodules is higher, so the segmentation of multiple nodules is also very important; (2) the ultrasound image of thyroid nodules is difficult to segment because of its low resolution and fuzzy boundary; (3) Due to the small data set of thyroid nodule ultrasound, in such a small dataset, attention mechanisms may be prone to overfitting, resulting in poorer generalization performance. Therefore, the attention-based model may not be suitable for the segmentation of thyroid nodules. Hence, we propose Hybrid Convolutional U-Net (HCU-Net), a simple and strong fully convolutional neural network for the segmentation of single or multiple thyroid nodules. HCU-Net incorporates a large receptive field to ensure accurate and stable segmentation. Specifically, we design Hybrid Branch Block to enhance the network's feature extraction capability for single or multiple nodules in ultrasound images by integrating multi-scale features. Furthermore, we add Enhanced ConvNeXt Blocks to the U-Net's final layer, enabling the extraction of global contextual information for segmentation performance. The Experimental results show that our proposed method can effectively improve these issues on two publicly available datasets. In addition, compared to attention-based models, our network is simpler and more suitable for small datasets like ultrasound images.

关键词： Image segmentation Ultrasonic imaging Image resolution Medical services Feature extraction Convolutional neural networks Medical diagnostic imaging Thyroid Overfitting Diseases

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Multi-Scale and Improved Skip Connections Network for Skin Lesion Segmentation

Multi-Scale and Improved Skip Connections Network for Skin L...

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6th International Conference on intelligent Computing and Signal processing (ICSP)

作者： Botao Zhang Jun Chen College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

To solve the problem that the current skin lesion segmentation networks have a poor segmentation accuracy when lesion areas are of different shapes and sizes and low contrast at the edge areas, based on U-Net, we present a skin lesion segmentation network based on multi-scale and improved skip connections. Firstly, a multi-scale feature enhancement module is designed to replace the U-Net's $\boldsymbol{3\times 3}$ convolution blocks, which can improve the feature extraction ability of the network about lesion areas of different shapes and sizes. Secondly, a lesion attention module is introduced into the skip connection to improve the semantic expression of low-level features and the location attention of high-level features, which can improve the fine-grained information transmission efficiency at the skip connection. Finally, a new hybrid loss function is designed to calculate the label and the edge loss of lesion areas, which improves the ability of the network to segment lesion areas and edges. Experiments are conducted on the ISIC2017 and ISIC2018 datasets, and the results prove that our method have a higher segmentation accuracy in skin lesion segmentation.

关键词： Accuracy Shape Convolution Semantics information processing Feature extraction Skin Lesions

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Drug-Target Interaction Prediction Based on Drug Subgraph Fingerprint Extraction Strategy and Subgraph Attention Mechanism 9th

Drug-Target Interaction Prediction Based on Drug Subgraph Fi...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Wang, Lizhi Zhang, Xiaolong Lin, Xiaoli Hu, Jing College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Realtime Industrial System Hubei Wuhan China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Hubei Wuhan China

ISBN: (纸本)9783031466700

Drug discovery is a major focus of modern research, and predicting drug-target interactions is one of the strategies to facilitate this research process. Traditional laboratory methods have long time cycles and are relatively costly, so the use of high-precision virtual screening is essential. Previous virtual screens have only considered the sequence structure of the drug or the graph structure of the drug, without considering the role of the drug subgraph structure. Therefore, this paper adopts a method for extracting drug subgraphs and designs a strategy for extracting drug subgraph fingerprints. Then, the fingerprint vector data of the drugs are trained by the graph neural network to produce drug information vectors containing the spatial structure of the drug subgraph. Finally, the subgraph attention mechanism is used to update the information on protein targets, facilitating the model to extract more information about the target site. The experimental results show that the model can predict drug-target interactions well, outperforming the state-of-the-art model in all metrics. And it is also effective in both classification prediction and affinity regression prediction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Graph neural networks

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User Behavior-Based Collaborative Filtering Recommendation Algorithm and Visualization in Second-Hand Trading Platform

User Behavior-Based Collaborative Filtering Recommendation A...

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Sensors, Electronics and Computer Engineering (ICSECE), IEEE International Conference on

作者： Rui Qiao Xin Wang Xiaoben Zhong School of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan Hubei China Yunnan College of Business Management Anning China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

In the backdrop of the digital era, the demand for second-hand trading within campuses has seen a significant rise. This project aims at designing a second-hand trading platform that merges the latest technological trends, providing a secure and convenient trading environment for students. The system utilizes Spring Boot for backend development and Vue along with ElementUI for constructing the user interface, ensuring efficient operation and enhancing user interaction experience. The server employs Spring Boot's built-in Tomcat, simplifying the deployment process. For data persistence, Mybatis framework is used for flexible and efficient interactions with the MySQL database. The platform introduces a collaborative filtering algorithm based on user behavior for smart product recommendations, further improving the user experience. The system integrates core modules such as user management, product browsing, shopping cart functionalities, and order processing, innovatively introducing an offline transaction confirmation mechanism to ensure transaction security and reliability while avoiding legal risks associated with online payments. With its B/S architecture, the platform operates more smoothly, making it more accessible to users. Experimental results demonstrate that the platform has a user-friendly interface and efficient data processing capabilities, receiving widespread recognition and praise from users, proving its practicality and innovativeness.

关键词： Law Collaborative filtering Market research User experience Sensor systems Servers Security Reliability Springs intelligent sensors

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A Novel Two-Stage Method for Texture Defect Detection

A Novel Two-Stage Method for Texture Defect Detection

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Lu Liu Min Jiang Lifa Xu School of Computer Science and Technology Wuhan University of Science and Technology WuHan Hubei P.R. China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan Hubei China

Texture defect detection has wide applications in both textiles and industry, where traditional defect detection methods focus on costly manual labeling and defect samples are complex to collect in real-world industrial scenarios. Furthermore, labeling errors are common in datasets from industrial scenarios, and the problem still needs to be explored. Therefore, we propose a novel texture defect detection method. It has two stages; the defective samples are synthesized in the first stage. In the second stage, we represent normal samples using a self-supervised learning method. Then, we construct a single classifier based on this learned representation. Subsequently, the model is trained to distinguish between normal and defective samples within the embedding space. To increase the robustness of our method, we adopt a novel strategy to construct defective samples and compute the feature similarity through a similarity-weighted aggregation method to achieve defect detection, which is more robust to noise. Experimental results on the DAGM2007 dataset show that our method performs comparably to the leading texture defect detection methods without label noise. In contrast, in the presence of noise, our method can effectively minimize the impact of noise on model performance.

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PL-TD3: A Dynamic Path Planning Algorithm of Mobile Robot

PL-TD3: A Dynamic Path Planning Algorithm of Mobile Robot

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2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Tan, Yijian Lin, Yunhan Liu, Tong Min, Huasong Wuhan University of Science and Technology College of Computer Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan430081 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

In this paper, Prioritized Experience Replay (PER) strategy and Long Short Term Memory (LSTM) neural network are introduced to the path planning process of mobile robots, which solves the problems of slow convergence and inaccurate perception of dynamic obstacles with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. We dubbed this new method as PL-TD3. Firstly, we improve the convergence speed of the algorithm by introducing PER strategy. Secondly, we use LSTM neural network to achieve the improvement of the algorithm for dynamic obstacle perception. In order to verify the method of this paper, we design static environment, dynamic environment and adaptability to dynamic experiments to compare and analyze the methods before and after improvement. The experimental results show that PL-TD3 outperforms TD3 in terms of execution time and execution path length in all environments. © 2022 IEEE.

关键词： Motion planning

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