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DSPE: An End-to-End Drug Synergy Combination Prediction Algorithm for Echinococcosis

Current Bioinformatics 2025年

作者： Li, Haitao Jiang, Liyuan Chu, Yuanyuan Liu, Yuansheng Zheng, Chunhou Su, Yansen Key Laboratory of Intelligent Computing and Signal Processing School of Artificial Intelligence Anhui University 111 Jiulong Road Hefei 230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center 5089 Wangjiang West Road Anhui Hefei 230088 China College of Computer Science and Electronic Engineering Hunan University China

Background: Echinococcosis, a parasitic disease caused by the larvae of the Echinococcus parasite, poses a serious threat to human health. Medication is an indispensable means of treatment for Echinococcosis;however, due to the less satisfactory efficacy of single drugs, identifying effective drug combinations for the treatment of Echinococcosis is essential. Yet, current predictive models for drug synergy in Echinococcosis face accuracy challenges due to data scarcity, method limitations, and insufficient feature representation. Objective: This work aims to design an end-to-end method to predict drug synergistic combinations, which enables efficient and accurate identification of drug combinations against Echinococcosis. Methods: In this work, an end-to-end method, named DSPE, is proposed for predicting anti-Echinococcosis drug synergistic combinations. In DSPE, a dataset of Echinococcosis drug synergistic combinations is constructed by retrieving and extracting information from related scientific articles. Further, DSPE employs a residual graph attention network to deeply analyze drug characteristics and their interactions, thereby enhancing the performance of deep learning models. It also explores the protein-protein interaction network related to Echinococcosis, using node2vec combined with an attention mechanism to efficiently encode disease features. Finally, it predicts the synergy of drug combinations based on the Bliss score by integrating drug combinations and disease features. Results: Experimental evidence shows that DSPE outperforms five state-of-the-art algorithms in predicting drug combination effects by leveraging disease-target information and single-agents for the treatment. Conclusion: DSPE improves prediction accuracy and addresses the issue of data scarcity for new diseases, offering new insights and methods for the development of treatment plans for parasitic diseases in the future. © 2025 Bentham Science Publishers.

关键词： Disease-target information Drug combination Drug synergistic combination prediction Echinococcosis Residual graph attention network Single drug

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Manifold feature extraction for video based on locally linear embedding

Manifold feature extraction for video based on locally linea...

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International Conference on Computer Science and Education

作者： Fu, Maosheng Xu, Qin Kong, Min Luo, Bin Key Lab. of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China Department of Computer Science and Technology West Anhui University Anhui Lu'an 237012 China

ISBN: (纸本)9781424460052

Locally linear embedding (LLE) is an elegant nonlinear method for feature extraction and manifold learning, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. However, LLE algorithm fails when it is directly applied to video with multi-shot. In this paper, video manifold feature is defined firstly, and then using LLE we extract video manifold feature through adding virtual frames on an enriched set. The proposed manifold feature extraction method is applied to shot transition and video trajectories. The video manifold feature description gives a new tool for video analysis. ©2010 IEEE.

关键词： Feature extraction

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An epipolar resticted inter-mode selection for stereoscopic video encoding

An epipolar resticted inter-mode selection for stereoscopic ...

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28th Picture Coding Symposium, PCS 2010

作者： Yang, Guolei Liang, Luhong Gao, Wen Peking University China Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781424471348

Fast stereoscopic video encoding becomes a highly desired technique because the stereoscopic video has been realizable for applications like TV broadcasting and consumer electronics. The stereoscopic video has high inter-view dependency subject to epipolar restriction, which can be used to reduce the encoding complexity. In this paper, we propose a fast inter-prediction mode selection algorithm for stereoscopic video encoding. Different from methods using disparity estimation, candidate modes are generated by sliding a window along the macro-block line restricted by the epipolar. Then the motion information is utilized to rectify the candidate modes. A selection failure handling algorithm is also proposed to preserve coding quality. The proposed algorithm is evaluated using independent H.264/AVC encoders for left and right views and can be extended to MVC. Experimental results show that encoding times of one view are reduced by 41.4% and 24.4% for HD and VGA videos respectively with little quality loss. © 2010 IEEE.

关键词： Encoding (symbols)

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A novel heterogeneous data integration approach for P2P semantic link network

A novel heterogeneous data integration approach for P2P sema...

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Thirteenth International World Wide Web Conference Proceedings, WWW2004

作者： Zhuge, Hai Liu, Jie China Knowledge Grid Research Group Key Lab of Intelligent Information Processing Institute of Computing Technology Beijing 100080 China

ISBN: (纸本)158113844X

This paper proposes a novel approach to integrate heterogeneous data in P2P networks. The approach includes a tool for building P2P semantic link networks, mechanisms for peer schema mapping, criteria for peer similarity degree measurement, and algorithms for heterogeneous data integration. The approach has three advantages: First, it uses semantic links to describe semantic relationships between peers' data schemas. Second, it deals with the semantic heterogeneity, the structural heterogeneity and the data value inconsistency. Finally, it considers the semantic similarity and structural similarity to forward queries to relevant peers.

关键词： Data reduction

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Dynamic clustering algorithm in mobile wireless sensor network

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Journal of Information and Computational Science 2015年第13期12卷 4897-4907页

作者： Wang, Bingting Liang, Hui Lin, Qibin Zhang, Yangyi Wang, Yujie Hong, Peilin Hu, Yanjun Department of Electronic and Electrical Engineering Chuzhou University Chuzhou China Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

In this paper, we analyzed the blindness of traditional clustering algorithms, which select cluster head based on residual energy. Then we proposed the Dynamic Clustering Algorithm (DCA) in Mobile Wireless Sensor Network, which takes into consideration residual buffer level when selecting cluster heads and allots time slots dynamically base on the occupied buffer level. We analyzed the energy model of the proposed DCA algorithm and calculated the optimal distance between cluster heads. Simulation results show that our DCA clustering algorithm has good performances in terms of residual energy, data transmission and network lifetime by comparing it with LEACH and LEACH-CI. ©, 2015, Journal of Information and Computational Science. All right reserved.

关键词： Clustering algorithms

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A novel approch for clustering of Chinese text based on concept hierarchy

A novel approch for clustering of Chinese text based on conc...

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IEEE International Conference on Cognitive Informatics

作者： Zhao, Peng Geng, Huan-Tong Cai, Qing-Sheng Department of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Key Lab. of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei 230039 China

ISBN: (纸本)9781424404759

After analyzing the disadvantages of traditional text clustering method based on keywords set, a novel approach for clustering of Chinese text based on concept hierarchy is presented. It introduces a Chinese topic classify dictionary as background knowledge to clustering of Chinese text. It adopts a hierarchical coding system which reflects concept relevance among different words and uses Vector Space Model based on concept hierarchy to represent Chinese text. The experimental results show this approach is more effective than traditional text clustering method based on keywords set. © 2006 IEEE.

关键词： Cluster analysis

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A Dual-band Low-profile Quadrifilar-Helix Antenna for Satellite Navigation System

A Dual-band Low-profile Quadrifilar-Helix Antenna for Satell...

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2023 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2023

作者： Wang, Jiahong Qi, Junwei Li, Yingsong Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9798350336740

A new dual-band low-profile quadrifilar-helical antenna (QHA) was presented. The spiral radiation arm of QHA is printed to three circular dielectric plates to reduce the antenna height. In this case, the radiation arm of the lower layer is connected to the feed, and the L-shaped branch is connected to the ground through a 50 ohm resistance. The radiation arm in the middle layer adds branches to realize dual frequency. The upper radiation arm rotates inward and folds, further realizing miniaturization. As a result, the height of QHA is only 20mm. Through CST simulation and optimization, The operating frequency bands of QHA are 1GHz - 1.45GHz and 1.54GHz - 1.74GHz. The beam width of QHA is more than 120°, which is to provide service for satellite navigating systems. The AR for the operation bands are less than 3dB. © 2023 IEEE.

关键词： Satellites

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Fast intra mode selection for stereo video coding using epipolar constraint

Fast intra mode selection for stereo video coding using epip...

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2011 IEEE Visual Communications and Image processing, VCIP 2011

作者： Yang, Guolei Liang, Luhong Gao, Wen Peking University Beijing China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781457713200

Application of stereo video in TV industry and consumer electronics becomes very popular recently. Thus, fast algorithm for stereo video coding is highly desired because of its huge inter-view computational redundancy. In our previous work we proposed an epipolar constraint based fast inter mode selection for stereo video using motion vector of blocks as a indicator of similarity, nevertheless, intra mode selection is also highly complex in standards like H.264/AVC. In this paper, a practical fast intra mode selection is proposed to eliminate the computational redundancy by exploiting inter-view dependency based on epipolar constraint. The proposed method does not rely on disparity estimation. Instead, a sliding window is employed to generate an intra mode candidate pool from macro-blocks on the epipolar line. The candidate pool is then rectified to remove invalid modes and improve accuracy. Finally, optimal prediction mode is selected from the candidate pool. The proposed method can significantly reduce the number of mode candidates/prediction directions compared to exhaustive mode selection by 79%. Experiments on 5 HD video coded in I-frame demonstrate the overall coding time of one view is saved by 56% on average, with slightly video quality loss less than 0.1dB. © 2011 IEEE.

关键词： Redundancy

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UPML-FDTD parallel computing on GPU

UPML-FDTD parallel computing on GPU

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2012 International Conference on Microwave and Millimeter Wave Technology, ICMMT 2012

作者： Ma, Weiwei Sun, Dong Wu, Xianliang Key of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei 230039 China School of Electrical Engineering and Automation Anhui University Heifei 230039 China

ISBN: (纸本)9781467321839

Finite-Difference Time-Domain method (FDTD) which is known with its simple and flexible is widely used for the calculating of electromagnetic fields. However, it costs a lot of time in simulating the electrical-large object. To sovle the promble, parallel method for UPML-FDTD algorithm was put forward using GPU based-on Compute Unified Device Architecture (CUDA) in this paper. The algorithm is further optimized by using texture memory. Comparing with the calculation with traditional CPU, The result of simulation shows that this algorithm has enough precision and the remarkable increased efficiency was acquired in different sizes of Yee cells. © 2012 IEEE.

关键词： Finite difference time domain method

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SES: Bridging the Gap between Explainability and Prediction of Graph Neural Networks 40

SES: Bridging the Gap between Explainability and Prediction ...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Huang, Zhenhua Li, Kunhao Wang, Shaojie Jia, Zhaohong Zhu, Wentao Mehrotra, Sharad Anhui University Hefei China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Hefei China Amazon Research Seattle United States University of California Irvine Irvine United States

ISBN: (纸本)9798350317152

Despite the Graph Neural Networks' (GNNs) pro-ficiency in analyzing graph data, achieving high-accuracy and interpretable predictions remains challenging. Existing GNN interpreters typically provide post-hoc explanations disjointed from GNNs' predictions, resulting in misrepresentations. Self-explainable GNNs offer built-in explanations during the training process. However, they cannot exploit the explanatory outcomes to augment prediction performance, and they fail to provide high-quality explanations of node features and require additional processes to generate explainable subgraphs, which is costly. To address the aforementioned limitations, we propose a self-explained and self-supervised graph neural network (SES) to bridge the gap between explainability and prediction. SES comprises two processes: explainable training and enhanced predictive learning. During explainable training, SES employs a global mask generator co-trained with a graph encoder and directly produces crucial structure and feature masks, reducing time consumption and providing node feature and subgraph explanations. In the enhanced predictive learning phase, mask-based positive-negative pairs are constructed utilizing the ex-planations to compute a triplet loss and enhance the node representations by contrastive learning. Extensive experiments demonstrate the superiority of SES on multiple datasets and tasks. SES outperforms baselines on real-world node classification datasets by notable margins of up to 2.59% and achieves state-of-the-art (SOTA) performance in explanation tasks on synthetic datasets with improvements of up to 3.0%. Moreover, SES delivers more coherent explanations on real-world datasets, has a fourfold increase in Fidelity+ score for explanation quality, and demonstrates faster training and expla-nation generating times. To our knowledge, SES is a pioneering GNN to achieve SOTA performance on both explanation and prediction tasks. © 2024 IEEE.

关键词： Classification (of information)

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