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Co-training based prediction of multi-label protein–protein interactions

Computers in Biology and Medicine 2024年 177卷 108623-108623页

作者： Tang, Tao Zhang, Xiaocai Li, Weizhuo Wang, Qing Liu, Yuansheng Cao, Xiaofeng School of Modern Posts Nanjing University of Posts and Telecommunications 9 Wenyuan Rd Jiangsu Nanjing210023 China 1 Fusionopolis Way Singapore138632 Singapore School of Management Nanjing University of Posts and Telecommunications 9 Wenyuan Rd Jiangsu Nanjing210023 China College of Computer Science and Electronic Engineering Hunan University 2 Lushan Rd Hunan Changsha410086 China Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education Anhui University 111 Jiulong Road Anhui Hefei230601 China School of Artificial Intelligence Jilin University 2699 Qianjin St Changchun Jilin130012 China

Prediction of protein–protein interaction (PPI) types enhances the comprehension of the underlying structural characteristics and functions of proteins, which gives rise to a multi-label classification problem. The nominal features describe the physicochemical characteristics of proteins directly, establishing a more robust correlation with the interaction types between proteins than ordered features. Motivated by this, we propose a multi-label PPI prediction model referred to as CoMPPI (Co-training based Multi-label prediction of Protein–Protein Interaction). This approach aims to maximize the utility of both ordered and nominal features extracted from protein sequences. Specifically, CoMPPI incorporates graph convolutional network (GCN) and 1D convolution operation to process the complementary subsets of features individually, leveraging both local and contextualized information in a more efficient way. In addition, two multi-type PPI datasets were constructed to eliminate the duplication in previous datasets. We compare the performance of CoMPPI with three state-of-the-art methods on three datasets partitioned using distinct schemes (Breadth-first search, Depth-first search, and Random), CoMPPI consistently outperforms the other methods across all cases, demonstrating improvements ranging from 3.81% to 32.40% in Micro-F1. The subsequent ablation experiment confirms the efficacy of employing the co-training framework for multi-label PPI prediction, indicating promising avenues for future advancements in this domain. © 2024 Elsevier Ltd

关键词： Proteins

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Direct speech-to-image translation

arXiv

引用

arXiv 2020年

作者： Li, Jiguo Zhang, Xinfeng Jia, Chuanmin Xu, Jizheng Zhang, Li Wang, Yue Ma, Siwei Gao, Wen Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China National Engineering Laboratory for Video Technology School of Electronic Engineering and Computer Science Peking University Beijing100871 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Institute of Digital Media School of Electronic Engineering and Computer Science Peking University Beijing100871 China Peng Cheng Lab Shenzhen China Bytedance Inc

Direct speech-to-image translation without text is an interesting and useful topic due to the potential applications in human-computer interaction, art creation, computer-aided design. etc. Not to mention that many languages have no writing form. However, as far as we know, it has not been well-studied how to translate the speech signals into images directly and how well they can be translated. In this paper, we attempt to translate the speech signals into the image signals without the transcription stage. Specifically, a speech encoder is designed to represent the input speech signals as an embedding feature, and it is trained with a pretrained image encoder using teacher-student learning to obtain better generalization ability on new classes. Subsequently, a stacked generative adversarial network is used to synthesize high-quality images conditioned on the embedding feature. Experimental results on both synthesized and real data show that our proposed method is effective to translate the raw speech signals into images without the middle text representation. Ablation study gives more insights about our method. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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New algorithm of target classification in polarimetric SAR

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Journal of Systems Engineering and Electronics 2008年第2期000卷

作者： Wang Yang Lu Jiaguo Wu Xianliang Wang Yang (Key Lab of Intelligent Computing & Signal Processing of Ministry of Education Anhui Univ. Heifei 230039 P. R. China The 38th Research Inst. China Electronic Technology Corporation Hefei 230031 P. R. China) Lu Jiaguo (The 38th Research Inst. China Electronic Technology Corporation Hefei 230031 P. R. China) Wu Xianliang (Key Lab of Intelligent Computing & Signal Processing of Ministry of Education Anhui Univ. Heifei 230039 P. R. China)

The different approaches used for target decomposition (TD) theory in radar polarimetry are reviewed and three main types of theorems are introduced: those based on Mueller matrix, those using an eigenvector analysis of the coherency matrix, and those employing coherent decomposition of the scattering matrix. Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated success in many fields. A new algorithm of target classification, by combining target decomposition and the support vector machine, is *** conduct the experiment, the polarimetric synthetic aperture radar (SAR) data are used. Experimental results show that it is feasible and efficient to target classification by applying target decomposition to extract scattering mechanisms, and the effects of kernel function and its parameters on the classification efficiency are significant.

关键词： polarimetric synthetic aperture radar target decomposition support vector machine target classifi-cation kernel function.

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Optimal combination of conflicting evidence based on genetic algorithm

引用

ICIC Express Letters 2011年第4 A期5卷 1145-1150页

作者： Jiang, Wen Peng, Jinye Deng, Yong School of Electronics and Information Northwestern Polytechnical University No. 127 Youyixi Road Xi'an 710072 China Hangzhou Key Lab of E-Business and Information Security Hangzhou Normal University No. 16 Xuelin Street Xiasha Higher Education Zone Hanzhou 310036 China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiangtan University Xiangtan 411105 China School of Computer and Information Sciences Southwest University No. 2 Tiansheng Road Beibei District Chongqing 400715 China School of Electronics and Information Technology Shanghai Jiao Tong University No. 800 Dongchuan Road Shanghai 200240 China

The Dempster-Shafer (D-S) evidence theory is widely used in many fields of information fusion. However, counter-intuitive results may be obtained by the classical Dempster combination rule when collected evidences are highly in conflict with each other. So an optimal weighted average information fusion method is proposed. Firstly, the global distance of the weighted evidence is presented based on the distance function. Secondly, the optimal weight of every evidence body was acquired by genetic algorithm, while the global distance is the minimum. Finally, the weighted average of the masses is combined by classical Dempster's rule. Compared with existing methods, the proposed optimal method can effectively handle conflicting evidence with better performance of convergence. Numerical examples are illustrated the effectiveness of the proposed method. ICIC International © 2011.

关键词： Genetic algorithms

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Aggregation of interval numbers in group decision making

引用

ICIC Express Letters 2011年第4 A期5卷 1057-1062页

作者： Deng, Yong Wu, Jiyi Sun, Xiaohong Xu, Peida Zhang, Yajuan College of Computer and Information Sciences Southwest University Tiansheng Road Beibei District Chongqing 400715 China Hangzhou Key Lab of E-Business and Information Security Hangzhou Normal University No. 16 Xuelin Street Xiasha Higher Education Zone Hanzhou 310036 China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiangtan University Xiangtan 411105 China College of Food Science and Technology Shanghai Ocean University Hu Cheng Loop-road Lingang New City Shanghai 201306 China School of Electronics and Information Technology Shanghai Jiao Tong University Dongchuan Rd. Shanghai 200240 China

A group of decision-makers may differ in their choice of alternatives while taking a decision. So, in any decision-making problem concerning decisions made by a group, the question arises how best we can aggregate individual choices into a general consensus choice. Due to the inherent uncertain property of the problems in the ambiguous decision domain, the decision makers may use interval numbers to represent their opinions in some situations. In this paper, a simple method is proposed to aggregate interval numbers. Some desirable properties of this method are also discussed. Finally, a numerical example is given to show the use of the proposed method. ICIC International © 2011.

关键词： Aggregates

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V3Det Challenge 2024 on Vast Vocabulary and Open Vocabulary Object Detection: Methods and Results

arXiv

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arXiv 2024年

作者： Wang, Jiaqi Zang, Yuhang Zhang, Pan Chu, Tao Cao, Yuhang Sun, Zeyi Liu, Ziyu Dong, Xiaoyi Wu, Tong Lin, Dahua Chen, Zeming Wang, Zhi Meng, Lingchen Yao, Wenhao Yang, Jianwei Wu, Sihong Chen, Zhineng Wu, Zuxuan Jiang, Yu-Gang Wu, Peixi Chai, Bosong Nie, Xuan Yan, Longquan Wang, Zeyu Zhou, Qifan Wang, Boning Huang, Jiaqi Xu, Zunnan Li, Xiu Yuan, Kehong Zu, Yanyan Hao, Jiayao Gao, Qiong Jiao, Licheng Shanghai AI Laboratory China Chinese University of Hong Kong Hong Kong Shanghai Jiao Tong University China Wuhan University China Shenzhen International Graduate School Tsinghua University China Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China Microsoft Research Redmond United States University of Science and Technology of China China Zhejiang University China Northwestern Polytechnical University China Northwest University China Tsinghua Shenzhen International Graduate School Tsinghua University China Intelligent Perception and Image Understanding Lab Xidian University China

Detecting objects in real-world scenes is a complex task due to various challenges, including the vast range of object categories, and potential encounters with previously unknown or unseen objects. The challenges necessitate the development of public benchmarks and challenges to advance the field of object detection. Inspired by the success of previous COCO [15] and LVIS [11] Challenges, we organize the V3Det Challenge 2024 in conjunction with the 4th Open World Vision Workshop: Visual Perception via Learning in an Open World (VPLOW) at CVPR 2024, Seattle, US. This challenge aims to push the boundaries of object detection research and encourage innovation in this field. The V3Det Challenge 2024 consists of two tracks: 1) Vast Vocabulary Object Detection: This track focuses on detecting objects from a large set of 13204 categories, testing the detection algorithm's ability to recognize and locate diverse objects. 2) Open Vocabulary Object Detection: This track goes a step further, requiring algorithms to detect objects from an open set of categories, including unknown objects. In the following sections, we will provide a comprehensive summary and analysis of the solutions submitted by participants. By analyzing the methods and solutions presented, we aim to inspire future research directions in vast vocabulary and open-vocabulary object detection, driving progress in this field. Challenge homepage: https://***/challenge. © 2024, CC BY-NC-SA.

关键词： Object detection

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Variational quantum eigensolver with linear depth problem-inspired ansatz for solving portfolio optimization in finance

arXiv

引用

arXiv 2024年

作者： Wang, Shengbin Wang, Peng Li, Guihui Zhao, Shubin Zhao, Dongyi Wang, Jing Fang, Yuan Dou, Menghan Gu, Yongjian Wu, Yu-Chun Guo, Guo-Ping Origin Quantum Computing Company Limited Anhui Hefei230026 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Anhui Hefei230026 China Intelligent Information Sensing and Processing Lab College of Electronic Engineering Ocean University of China Shandong Qingdao266000 China College of Physics and Optoelectronic Engineering Ocean University of China Shandong Qingdao266100 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230088 China

Great efforts have been dedicated in recent years to explore practical applications for noisy intermediate-scale quantum (NISQ) computers, which is a fundamental and challenging problem in quantum computing. As one of the most promising methods, the variational quantum eigensolver (VQE) has been extensively studied. In this paper, VQE is applied to solve portfolio optimization problems in finance by designing two hardware-efficient Dicke state ansatze that reach a maximum of 2n two-qubit 2 gate depth and n2/4 parameters, with n being the number of qubits used. Both ansatze are partitioning-friendly, allowing for the proposal of a highly scalable quantum/classical hybrid distributed computing (HDC) scheme. Combining simultaneous sampling, problem-specific measurement error mitigation, and fragment reuse techniques, we successfully implement the HDC experiments on a superconducting quantum computer with up to 55 qubits. The simulation and experimental results illustrate that the restricted expressibility of the ansatze, induced by the small number of parameters and limited entanglement, is advantageous for solving classical optimization problems with the cost function of the conditional value-at-risk (CVaR) for the NISQ era and beyond. Furthermore, the HDC scheme shows great potential for achieving quantum advantage in the NISQ era. We hope that the heuristic idea presented in this paper can motivate fruitful investigations in current and future quantum computing paradigms. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization

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VANER: Leveraging Large Language Model for Versatile and Adaptive Biomedical Named Entity Recognition

arXiv

引用

arXiv 2024年

作者： Bian, Junyi Zhai, Weiqi Huang, Xiaodi Zheng, Jiaxuan Zhu, Shanfeng School of Computer Science Fudan University Shanghai200433 China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University China Ministry of Education Shanghai200433 China MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China Zhangjiang Fudan International Innovation Center Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai200433 China School of Computing and Mathematics Charles Sturt University AlburyNSW2640 Australia

Prevalent solution for BioNER involves using representation learning techniques coupled with sequence labeling. However, such methods are inherently task-specific, demonstrate poor generalizability, and often require dedicated model for each dataset. To leverage the versatile capabilities of recently remarkable large language models (LLMs), several endeavors have explored generative approaches to entity extraction. Yet, these approaches often fall short of the effectiveness of previouly sequence labeling approaches. In this paper, we utilize the open-sourced LLM LLaMA2 as the backbone model, and design specific instructions to distinguish between different types of entities and datasets. By combining the LLM’s understanding of instructions with sequence labeling techniques, we use mix of datasets to train a model capable of extracting various types of entities. Given that the backbone LLMs lacks specialized medical knowledge, we also integrate external entity knowledge bases and employ instruction tuning to compel the model to densely recognize carefully curated entities. Our model VANER, trained with a small partition of parameters, significantly outperforms previous LLMs-based models and, for the first time, as a model based on LLM, surpasses the majority of conventional state-of-the-art BioNER systems, achieving the highest F1 scores across three datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational linguistics

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Improving the trainability of VQE on NISQ computers for solving portfolio optimization using convex interpolation

arXiv

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arXiv 2024年

作者： Wang, Shengbin Li, Guihui Chen, Zhaoyun Wang, Peng Dou, Menghan Zheng, Haiyong Wang, Zhimin Gu, Yongjian Wu, Yu-Chun Guo, Guo-Ping Origin Quantum Computing Company Limited Anhui Hefei230026 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Anhui Hefei230026 China Intelligent Information Sensing and Processing Lab College of Electronic Engineering Ocean University of China Shandong Qingdao266000 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230088 China College of Physics and Optoelectronic Engineering Ocean University of China Shandong Qingdao266100 China

Solving combinatorial optimization problems using variational quantum algorithms (VQAs) represents one of the most promising applications in the NISQ era. However, the limited trainability of VQAs could hinder their scalability to large problem sizes. In this paper, we improve the trainability of variational quantum eigensolver (VQE) by utilizing convex interpolation to solve portfolio optimization. The idea is inspired by the observation that the Dicke state possesses an inherent clustering property. Consequently, the energy of a state with a larger Hamming distance from the ground state intuitively results in a greater energy gap away from the ground state energy in the overall distribution trend. Based on convex interpolation, the location of the ground state can be evaluated by learning the property of a small subset of basis states in the Hilbert space. This enlightens naturally the proposals of the strategies of close-to-solution initialization, regular cost function landscape, and recursive ansatz equilibrium partition. The successfully implementation of a 40-qubit experiment using only 10 superconducting qubits demonstrates the effectiveness of our proposals. Furthermore, the quantum inspiration has also spurred the development of a prototype greedy algorithm. Extensive numerical simulations indicate that the hybridization of VQE and greedy algorithms achieves a mutual complementarity, combining the advantages of both global and local optimization methods. Our proposals can be extended to improve the trainability for solving other large-scale combinatorial optimization problems that are widely used in real applications, paving the way to unleash quantum advantages of NISQ computers in the near future. Copyright © 2024, The Authors. All rights reserved.

关键词： Ground state

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Glycan immunogenicity prediction based on Graph neural network

Glycan immunogenicity prediction based on Graph neural netwo...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yu Wang Hui Wang Meijie Hou Yaojun Wang Dongbo Bu Chunming Zhang Chuncui Huang Shiwei Sun Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences 6 Kexueyuan South Road Beijing China 19 Yuquan Road University of Chinese Academy of Sciences Beijing China College of Information and Electrical Engineering China Agricultural University 17 Qinghua Donglu Beijing China Zhongke Big Data Academy Zhengzhou Henan China Phil Rivers Technology Beijing China 15 Datun Road Institute of Biophysics Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665429825

Glycans play important roles in a great variety of biological processes, and these roles are closely determined by the details of their structures. It becomes possible to acquire hidden features from glycan structures using deep learning method with the great progress in recent years. Unlike the linear chain of proteins and DNAs, branching is a unique feature of glycan structures, which makes it very difficult to directly apply deep learning models on glycans. Thus, how to comprehensively and efficiently describe glycans and use them as input to deep learning models still remains challenging. Here, a graph neural network (GNN) called GlyNet was used to obtain high-dimensional representation of glycans and predict their immunogenicity. Our method was applied in SugarBase, and it works more superiorly than the state-of-art method with accuracy increased from 91.7% to 95.6%.

关键词： Deep learning Proteins Spectroscopy Databases Conferences Biological processes Ions

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