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SSIG: Single-Sample Information Gain Model for Integrating Multi-Omics Data to Identify Cancer Subtypes

Chinese Journal of Electronics 2021年第2期30卷 303-312页

作者： ZHANG Yuanyuan WANG Ziqi WANG Shudong KOU Chuanhua School of Information and Control Engineering Qingdao University of Technology College of Computer Science and Technology China University of Petroleum (East China)

Different living environments of cancer samples lead to different molecular mechanisms of cancer development, which in turn leads to different cancer subtypes. How to identify cancer subtypes is a key issue for the realization of precision medicine. With the development of high-throughput technologies, multi-omics data which can better understand different causes of cancer have emerged. However, the current methods of analyzing cancer subtypes using multi-omics data is mostly derived from population cancer sample data and ignores the differences between different cancer ***, the joint analysis of multi-omics based on a single sample may reveal more information about the differences between individual cancers. A strategy for identifying cancer subtypes is proposed based on Single-sample information gain(SSIG) which construct sample feature matrix by considering the heterogeneity of sample. Applying this strategy to current popular subtype identification methods, cancer subtypes can be identified more accurately and the mechanism of cancer can be found from the perspective of a single sample. By comparing different methods in different clustering measure, and using survival analysis, it is shown that SSIG is more suitable for cancer subtype identification than the original multi-omics data, and it is easier to mine the cancer subtype classification mechanism hidden behind the data.

关键词： identify cancer subtypes SSIG medical computing cancer subtype classification mechanism Single-sample information gain model different cancer subtypes original multiomics data genetics cancer subtype identification current popular subtype identification methods population cancer sample data different cancer samples data analysis cancer development genomics cancer sample feature matrix different molecular mechanisms different living environments bioinformatics individual cancers different clustering measure

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A MATHEMATICAL APPROACH TO DESYNCHRONIZATION OF COUPLED OSCILLATORS： APPLICATION TO A NEURONAL ENSEMBLE

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International Journal of Biomathematics 2013年第2期6卷 173-189页

作者： GHAZAL MONTASERI ARAS ADHAMI-MIRHOSSEINI MOHAMMAD JAVAD YAZDANPANAH Advanced Control Systems Laboratory School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Advanced Control Systems LaboratoryControl and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

Synchronization of neurons plays an important role in vision, movement and memory. However, many neurological disorders such as epilepsies, Parkinson disease and essen- tial tremor are related to excessive synchronization of neurons. In the line of therapy, stimulations to these pathologically synchronized neurons should be capable of breaking synchrony. As the first step of designing an effective stimulation, we consider desynchro- nization problem of coupled limit-cycle oscillators ensemble. First, the desynchronization problem is redefined in a nonlinear output regulation framework. Then, we design an output regulator （stimulation） which forces limit-cycle oscillators to track exogenous sinusoidal references with different phases. The proposed stimulation is robust against variations of oscillators＇ frequencies. Mathematical analysis and simulation results reveal the efficiency of the proposed technique.

关键词： Limit-cycle oscillators nonlinear output regulation synchrony suppression robust stimulation.

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Design of equivalent-input-disturbance estimator using a generalized state observer

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控制理论与应用（英文版） 2013年第1期11卷 74-79页

作者： Min WU Kunpeng LOU Fangchun XIAO Ruijuan LIU Yong HE Jinhua SHE School of Information Science and Engineering Central South University Hunan Engineering Laboratory for Advanced Control and Intelligent Automation School of Computer Science Tokyo University of Technology

In this paper, we present a design method based on the concept of equivalent input disturbance （EID） to reject disturbances for a linear time-invariant system. A generalized state observer （GSO） is used to estimate an EID of the external disturbances, and the pole-assignment algorithm is employed to select the matrices of the GSO. Simulation and experimental results of a rotational speed control system demonstrate the validity of our method.

关键词： Disturbance estimation Disturbance rejection Equivalent input disturbance Generalized state observer Pole assignment Rotational speed control

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Text Generation of Speech Imagery Based on an Enhanced CTA-BiLSTM Model Utilizing EEG Signals

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IEEE Transactions on Consumer Electronics 2025年

作者： Pan, Hongguang Chu, Xin Miao, Rui Wang, Mei Wang, Yiran Li, Zhuoyi College of Electrical and Control Engineering Xi’an University of Science and Technology Xi’an710054 China Xi’an Key Laboratory of Electrical Equipment Condition Monitoring and Power Supply Security Xi’an710054 China LTD Company of Shenzhen HQVT Technology Co Shenzhen518100 China College of Computer Science and Technology Xi’an University of Science and Technology Xi’an710054 China Northwestern Polytechnic University School of Automation Xi’an710072 China

Recent studies have demonstrated the potential application of speech imagery neural signals in brain–computer interface (BCI) technology. Text generation based on speech imagery offers a natural communication method for individuals with speech disabilities. However, the limitations in imagined content and the immaturity of text generation technology currently constitute an obstacle to its applications. Therefore, this study proposes an enhanced CTA-BiLSTM model for efficient text generation utilizing speech imagery electroencephalography (EEG) signals, significantly enhancing the accuracy and fluency of text generation. Firstly, distinct from the prevailing imagination of characters and words, this study has assembled a sentence-level EEG dataset from ten subjects to facilitate communication. Subsequently, addressing the temporal dynamics characteristics and sequence dependencies of sentence signals, we employ dynamic time warping (DTW) and hidden Markov models (HMM) for accurate temporal alignment and signal annotation to generate fine-grained sentence labels. Finally, the proposed CTA-BiLSTM model leverages channel-time attention mechanism to dynamically adjust weights across channels and time, emphasizing critical features. Concurrently, the bidirectional long short-term memory (BiLSTM) network captures and utilizes long-term dependencies in the EEG signals, thereby enhancing the accuracy of the model in decoding complex temporal patterns. The experimental results demonstrate that the average sentence decoding accuracy can reach 67.50% on the self-built dataset, realizing a better evaluation accuracy and validating its potential for application. © 1975-2011 IEEE.

关键词： Hidden Markov models

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NONLINEAR control WITH END-POINT ACCELERATION FEEDBACK FOR A 2-LINK FLEXIBLE MANIPULATOR - EXPERIMENTAL RESULTS

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JOURNAL OF ROBOTIC SYSTEMS 1993年第4期10卷 505-530页

作者： KHORRAMI, F JAIN, S Control/Robotics Research Laboratory School of Electrical Engineering & Computer Science Polytechnic University Brooklyn NY 11201

Experimental results for end-point positioning of multi-link flexible manipulators through end-point acceleration feedback are presented in this article. The advocated controllers are implemented on a two-link flexible arm developed at the control/Robotics Research Laboratory at Polytechnic University. The advocated approach in this article is based on a two-stage control design. The first stage is a nonlinear O(1) feedback linearizing controller corresponding to the rigid body motion of the manipulator. Because this scheme does not utilize any feedback from the end-point motion, significant vibrations are induced at the end effector. To this effect, and to enhance the robustness of the closed-loop dynamics to parameter variations, the inner loop is augmented with an outer loop based on a linear output LQR design that utilizes an end-point acceleration feedback. The forearm of the manipulator is significantly more flexible as compared with the upper arm. Experimental and simulation results validate the fact that the end-effector performance is significantly better with the proposed O(1) feedback linearizing control as compared with the linear independent joint PD control. In addition, the nonlinear control offers other advantages in terms of smaller and smoother actuator torques and reducing the effects of nonlinearities. Close conformation between simulation and experimental results validates the accuracy of the model.

关键词： Manipulators

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Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning

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Chinese Journal of Electronics 2022年第2期31卷 345-353页

作者： WANG Yueyue LEI Xiujuan PAN Yi School of Computer Science Shaanxi Normal University Department of Computer Science Georgia State University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences

Numerous microbes inhabit human body,making a vast difference in human health. Hence, discovering associations between microbes and diseases is beneficial to disease prevention and treatment. In this study,we develop a prediction method by learning global graph feature on the heterogeneous network(called HNGFL).Firstly, a heterogeneous network is integrated by known microbe-disease associations and multiple *** on microbe Gaussian interaction profile(GIP) kernel similarity, we consider different effects of these microbes on organs in the human body to further improve microbe similarity. For disease similarity network, we combine GIP kernel similarity, disease semantic similarity and disease-symptom similarity. And then, an embedding algorithm called GraRep is used to learn global structural information for this network. According to vector feature of every node, we utilize support vector machine classifier to calculate the score for each microbe-disease pair. HNGFL achieves a reliable performance in cross validation, outperforming the compared methods. In addition, we carry out case studies of three diseases. Results show that HNGFL can be considered as a reliable method for microbe-disease association prediction.

关键词： microorganisms global graph feature learning support vector machines patient diagnosis global structural information disease semantic similarity disease-symptom similarity HNGFL pattern classification graph theory microbe Gaussian interaction profile kernel similarity bioinformatics support vector machine classifier GraRep diseases learning (artificial intelligence) microbe-disease association prediction heterogeneous network Gaussian processes GIP kernel similarity

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Dexterous Manipulation through Imitation Learning: A Survey

arXiv

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arXiv 2025年

作者： An, Shan Meng, Ziyu Tang, Chao Zhou, Yuning Liu, Tengyu Ding, Fangqiang Zhang, Shufang Mu, Yao Song, Ran Zhang, Wei Hou, Zeng-Guang Zhang, Hong School of Electrical and Information Engineering Tianjin University Tianjin300072 China School of Control Science and Engineering Shandong University Jinan250061 China State Key Laboratory of General Artificial Intelligence Beijing100086 China Department of Mechanical and Process Engineering ETH Zurich Zurich8092 Switzerland Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China School of Informatics University of Edinburgh EH8 9AB United Kingdom Department of Computer Science University of Hong Kong 999077 Hong Kong State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China CASIA-MUST Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology 999078 China

Dexterous manipulation, which refers to the ability of a robotic hand or multi-fingered end-effector to skillfully control, reorient, and manipulate objects through precise, coordinated finger movements and adaptive force modulation, enables complex interactions similar to human hand dexterity. With recent advances in robotics and machine learning, there is a growing demand for these systems to operate in complex and unstructured environments. Traditional model-based approaches struggle to generalize across tasks and object variations due to the high-dimensionality and complex contact dynamics of dexterous manipulation. Although model-free methods such as reinforcement learning (RL) show promise, they require extensive training, large-scale interaction data, and carefully designed rewards for stability and effectiveness. Imitation learning (IL) offers an alternative by allowing robots to acquire dexterous manipulation skills directly from expert demonstrations, capturing fine-grained coordination and contact dynamics while bypassing the need for explicit modeling and large-scale trial-and-error. This survey provides an overview of dexterous manipulation methods based on imitation learning (IL), details recent advances, and addresses key challenges in the field. Additionally, it explores potential research directions to enhance IL-driven dexterous manipulation. Our goal is to offer researchers and practitioners a comprehensive introduction to this rapidly evolving domain. Copyright © 2025, The Authors. All rights reserved.

关键词： Grippers

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An Encoding-Decoding Framework Based on CNN for circ RNA-RBP Binding Sites Prediction

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Chinese Journal of Electronics 2024年第1期33卷 256-263页

作者： Yajing GUO Xiujuan LEI Yi PAN School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Department of Computer Science Georgia State University

Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.

关键词： Circular RNAs (circRNAs) RNA binding proteins Convolutional neural network Temporal convolutional network Encoder-decoder network

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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H_∞ consensus control of a class of second-order multi-agent systems without relative velocity measurement

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Chinese Physics B 2010年第7期19卷 188-192页

作者：张文广曾德良郭振凯 Beijing Key Laboratory of Measurement and Control New Technology and System for Industrial Process Control & Computer Engineering SchoolNorth China Electric Power University School of Mathematics and Information Ludong University

This paper studies consensus control problems for a class of second-order multi-agent systems without relative velocity measurement. Some dynamic neighbour-based rules are adopted for the agents in the presence of external disturbances. A sufficient condition is derived to make all agents achieve consensus while satisfying desired H∞ performance. Finally, numerical simulations are provided to show the effectiveness of our theoretical results.

关键词： consensus control H∞ second-order multi-agent systems

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