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Pan-cancer network disorders revealed by overall and local signaling entropy

Journal of Molecular Cell Biology 2021年第9期13卷 622-635页

作者： Li Feng Yi-Di Sun Chen Li Yi-Xue Li Luo-Nan Chen Rong Zeng CAS Key Laboratory of Systems Biology CAS Center for Excellence in Molecular Cell ScienceInstitute of Biochemistry and Cell BiologyChinese Academy of SciencesShanghai 200031China University of Chinese Academy of Sciences Shanghai 200031China Institute of Neuroscience CAS Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai 200031China Center for Single-Celt Omics School of Public HealthShanghai Jiao Tong University School of MedicineShanghai 200025China Bio-Med Big Data Center CAS Key Laboratory of Computational BiologyCAS-MPG Partner Institute for Computational BiologyShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China CAS Key Laboratory of Systems Biology Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesChinese Academy of SciencesHangzhou 310024China

Tumor development is a process involving loss of the differentiation phenotype and acquisition of stem-like characteristics,which is driven by intracellular rewiring of signaling *** measurement of network reprogramming and disorder would be challenging due to the complexity and heterogeneity of ***,we proposed signaling entropy(SR)to assess the degree of tumor network *** calculated SR for 33 tumor types in The Cancer Genome Atlas database based on transcrip-tomic and proteomic *** SR of tumors was significantly higher than that of normal samples and was highly correlated with cell sternness,cancer type,tumor grade,and *** further demonstrated the sensitivity and accuracy of using local SR in prognosis prediction and drug response ***,SR could reveal cancer network disorders related to tumor malignant potency,clinical prognosis,and drug response.

关键词： pan-cancer network entropy

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Channel Exchanging Networks for Multimodal and Multitask Dense Image Prediction

arXiv

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arXiv 2021年

作者： Wang, Yikai Sun, Fuchun Huang, Wenbing He, Fengxiang Tao, Dacheng State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China JD Explore Academy *** Inc China

Multimodal fusion and multitask learning are two vital topics in machine learning. Despite the fruitful progress, existing methods for both problems are still brittle to the same challenge—it remains dilemmatic to integrate the common information across modalities (resp. tasks) meanwhile preserving the specific patterns of each modality (resp. task). Besides, while they are actually closely related to each other, multimodal fusion and multitask learning are rarely explored within the same methodological framework before. In this paper, we propose Channel-Exchanging-Network (CEN) which is self-adaptive, parameter-free, and more importantly, applicable for multimodal and multitask dense image prediction. At its core, CEN adaptively exchanges channels between subnetworks of different modalities. Specifically, the channel exchanging process is self-guided by individual channel importance that is measured by the magnitude of Batch-Normalization (BN) scaling factor during training. For the application of dense image prediction, the validity of CEN is tested by four different scenarios: multimodal fusion, cycle multimodal fusion, multitask learning, and multimodal multitask learning. Extensive experiments on semantic segmentation via RGB-D data and image translation through multi-domain input verify the effectiveness of CEN compared to state-of-the-art methods. Detailed ablation studies have also been carried out, which demonstrate the advantage of each component we propose. Our code is available at https://***/yikaiw/CEN. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantic Segmentation

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A Causal Adversarial Graph Neural Network for Multi-Center Autism Spectrum Disorder Identification

SSRN

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SSRN 2025年

作者： Zhang, Zhuan Wang, Lihui Chen, Qijian Wang, Li Jian, Caiqing Zhu, Yue-Min Wei, Hongjiang Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China University Lyon INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 Lyon69621 France

Due to the heterogeneous distributions of multi-center rs-fMRI data, currently it is still challenging to identify accurately autism spectrum disorder (ASD) patients from these heterogenous data. To deal with the influence of heterogeneous distributions on multi-center ASD diagnosis, a novel causal adversarial graph neural network (CAG) for multi-center ASD prediction is proposed in this work. CAG mainly consists of a graph filtering (GF) module and a learnable causal graph predictor (CGP). Specifically, the brain connection graph is first constructed from rs-fMRI data, after passing a GF module, the filtered graph is further divided by a causal subgraph and causally irrelevant subgraph. The ground-truth environmental labels and ASD labels are predicted from causal subgraph by a clustering method and a classification block, respectively. In addition, from the filtered graph and causally irrelevant subgraph, the environmental labels are predicted, while from the causally irrelevant subgraph the ASD labels are predicted. Assuming that the predicted environmental labels are independent to causal subgraph, while the predicted ASD labels are independent to causally irrelevant subgraph, accordingly, several adversarial losses are implemented by reversing the gradient. Through this adversarial learning strategy, the derived causal graph can predict multi-center ASD. The comparison experiments on both ABIDE-I and ABIDE-II datasets demonstrate that, the ACC and AUC of CAG has been improved by 3.78\% and 7.38\% at least comparing against the existing methods. Moreover, some abnormal functional connectivities in ASD group are identified from the causal subgraph which may provide useful imaging biomarkers for the early diagnosis of ASD. © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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Regional attention with architecture-rebuilt 3D network for RGB-D gesture recognition

arXiv

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arXiv 2021年

作者： Zhou, Benjia Li, Yunan Wan, Jun Macau University of Science and Technology China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China School of Computer Science and Technology Xidian Univeristy China Xi'an Key Laboratory of Big Data and Intelligent Vision China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

Human gesture recognition has drawn much attention in the area of computer vision. However, the performance of gesture recognition is always influenced by some gesture-irrelevant factors like the background and the clothes of performers. Therefore, focusing on the regions of hand/arm is important to the gesture recognition. Meanwhile, a more adaptive architecture-searched network structure can also perform better than the block-fixed ones like ResNet since it increases the diversity of features in different stages of the network better. In this paper, we propose a regional attention with architecture-rebuilt 3D network (RAAR3DNet) for gesture recognition. We replace the fixed Inception modules with the automatically rebuilt structure through the network via Neural Architecture Search (NAS), owing to the different shape and representation ability of features in the early, middle, and late stage of the network. It enables the network to capture different levels of feature representations at different layers more adaptively. Meanwhile, we also design a stackable regional attention module called Dynamic-Static Attention (DSA), which derives a Gaussian guidance heatmap and dynamic motion map to highlight the hand/arm regions and the motion information in the spatial and temporal domains, respectively. Extensive experiments on two recent large-scale RGB-D gesture datasets validate the effectiveness of the proposed method and show it outperforms state-of-the-art methods. The codes of our method are available at: https://***/zhoubenjia/RAAR3DNet. Copyright © 2021, The Authors. All rights reserved.

关键词： Large dataset

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HCD: A Hierarchy Constraint-Aware Neural cognitive Diagnosis Framework

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Neural Networks 2025年 190卷 107668页

作者： Sun, Xinjie Liu, Qi Zhang, Kai Shen, Shuanghong Wang, Fei Zhuang, Yan Zhang, Zheng Gong, Weiyin Wang, Shijin Yang, Lina Huo, Xingying School of Computer Science and Technology University of Science and Technology of China Hefei China School of Computer Science Liupanshui Normal University Liupanshui China School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei China State Key Laboratory of Cognitive Intelligence Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China School of Computing National University of Singapore Singapore Singapore Hefei China

cognitive diagnosis (CD) aims to reveal students’ proficiency in specific knowledge concepts. With the increasing adoption of intelligent education applications, accurately assessing students’ knowledge mastery has become an urgent challenge. Although existing cognitive diagnosis frameworks enhance diagnostic accuracy by analyzing students’ explicit response records, they primarily focus on individual knowledge state, failing to adequately reflect the relative ability performance of students within hierarchies. To address this, we propose the Hierarchy Constraint-Aware Neural cognitive Diagnosis Framework (HCD), designed to more accurately represent student ability performance within real educational contexts. Specifically, the framework introduces a hierarchy mapping layer to identify students’ levels. It then employs a hierarchy convolution-enhanced attention layer for in-depth analysis of knowledge concepts performance among students at the same level, uncovering nuanced differences. A hierarchy inter-sampling attention layer captures performance differences across hierarchies, offering a comprehensive understanding of the relationships among students’ knowledge state. Finally, through personalized diagnostic enhancement, the framework seamlessly integrates hierarchy constraint-aware features with existing typical diagnostic methods, significantly improving the precision of student knowledge state representation and enhancing the adaptability and diagnostic performance of existing frameworks. Research shows that this framework not only reasonably constrains changes in students’ knowledge state to align with real educational contexts, but also supports the scientific rigor and fairness of educational assessments, thereby advancing the field of cognitive diagnosis. To support reproducible research, we have published the data and code at https://***/xinjiesun-ustc/HCD, encouraging further innovation in this field. © 2025 Elsevier Ltd

关键词： Students

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Optimization of the Spatial Location of Sound Sensors for Urban Noise Monitoring

SSRN

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SSRN 2023年

作者： Cheng, Jin Zhao, Yanjie Wang, Shaohua Wang, Zhenbo Qin, Lei Liang, Haojian Su, Cheng Li, Xiao Research Center for Sensor Technology School of Applied Sciences Jianxiangqiao Campus Beijing Information Science and Technology University Beijing100101 China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China Beijing Key Laboratory for Optoelectronic Measurement Technology Beijing Information Science & Technology University Beijing100192 China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science & Technology University Beijing100192 China School of Artificial Intelligence Jilin University Changchun130012 China Faculty of Geomatics Lanzhou Jiaotong University Lanzhou730070 China

Urban noise measurement is an important way to achieve Urban Sustainable Development. The noise levels can be measured scientifically, thereby enabling the further development of noise management strategies. However, urban noise measurement is a complex and challenging issue. In this study, we constructed a spatial location optimization model for sound sensors, BCSSLP, the intention was to employ mixed-integer linear programming, to determine the optimal locations of sound sensors for noise monitoring in Chicago. BCSSLP took full consideration of existing and new sound sensors' collaborative effects. We used two methods, namely the Gurobi solver and the genetic algorithm, to construct the model. We determined the most effective sound sensor deployment quantity and location by comparing the coverage with various deployment quantities. Our experimental results show that in Chicago, 50.88% of the total population can be covered by 65 sound sensors. That is, an increase of 30 sound sensors brings the coverage rate above the 50% threshold. Additionally, 28.94% of the population (104 demand points) can be monitored by two or more sound sensors, representing a significant improvement over the current situation. Furthermore, due to the unevenly distributed population in Chicago, increasing the number of sound sensors does not significantly expand the coverage area for the population. Therefore, deploying more sound sensors for noise monitoring yields little improvement. This study demonstrates that the model can significantly improve backup coverage with minimal primary coverage loss. By selecting appropriate locations, larger coverage of noise monitoring can be achieved with minimal numbers of sound sensors. © 2023, The Authors. All rights reserved.

关键词： Location

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Asynchronous control for Markov jump systems subject to actuator saturation

Asynchronous control for Markov jump systems subject to actu...

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第33届中国控制与决策会议

作者： Chao Wang Lei Liu Wei Wang Xiaobing Luo Huijin Fan the National Key Laboratory of Science and Technology of Multispectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and Technology the School of Automation Science and Electrical Engineering and the Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University School of Computer Technology and Software Engineering Wuhan Polytechnic

In this paper,the asynchronous controller problem is considered for Markov jump systems(MJSs) subject to actuator *** asynchronous phenomenon between the controller and the plant is addressed by introducing a hidden Markov model and the closed-loop hidden MJSs are *** on the closed-loop hidden MJSs and using convex hull technique and linear matrix inequality technique,a controller design algorithm is derived to ensure that the closed-loop hidden MJSs satisfy the stochastic ***,a numerical example is provided to demonstrate the effectiveness of the proposed asynchronous controller design algorithm.

关键词： Asynchronous control actuator saturation Markov jump systems

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Simultaneous Deep Learning of Myocardium Segmentation and T2 Quantification for Acute Myocardial Infarction MRI

arXiv

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arXiv 2024年

作者： Zhou, Yirong Wang, Chengyan Lu, Mengtian Guo, Kunyuan Wang, Zi Ruan, Dan Guo, Rui Zhao, Peijun Wang, Jianhua Wu, Naiming Lin, Jianzhong Chen, Yinyin Jin, Hang Xie, Lianxin Wu, Lilan Zhu, Liuhong Zhou, Jianjun Cai, Congbo Wang, He Qu, Xiaobo Department of Electronic Science Intelligent Medical Imaging R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University Xiamen361104 China Human Phenome Institute Fudan University Shanghai201210 China Xianning437100 China School of Medical Technology Beijing Institute of Technology Beijing100081 China Department of Radiology The First Affiliated Hospital of Xiamen University School of Medicine Xiamen University Xiamen361003 China Department of Radiology Xiamen Cardiovascular Hospital Xiamen University School of Medicine Xiamen University Xiamen361004 China Department of Radiology Zhongshan Hospital Affiliated to Xiamen University School of Medicine Xiamen University Xiamen361004 China Department of Radiology Zhongshan Hospital Shanghai200032 China Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence Xiamen361006 China

In cardiac Magnetic Resonance Imaging (MRI) analysis, T2 mapping is a tissue quantification technology to measure water and inflammation levels and has been recognized as an important versatile index for myocardial pathologies. The T2 quantification is a highly relevant task with the myocardial segmentation but they only have been explored separately in deep learning. Here, we proposed a simultaneous dual-task network for myocardial segmentation and T2 quantification, called SQNet. It integrates Transformer and Convolutional Neural Network (CNN) components. SQNet has a T2-refine fusion decoder for quantitative analysis, incorporating global features extracted by the Transformer, and employs a segmentation decoder with multiple local region supervision to enhance segmentation accuracy. Additionally, SQNet proposes a tight coupling module that aligns and fuses CNN and Transformer branch features. The mutual promotion of the two tasks enables the SQNet to focus on the myocardium regions. The dual-task performance is validated on healthy controls (HC, N=17) and acute myocardial infarction patients (AMI, N=12). The segmentation dice of SQNet on HC/AMI is 89.3/89.2, which is higher than that (87.7/87.9) of the compared state-of-the-art segmentation method. The Pearson correlation coefficient of the T2 value on HC/AMI is 0.84 and 0.93 to the label value, indicating a strong linear correlation in myocardial T2 mapping. From the clinical diagnostic perspective, eight experienced radiologists have evaluated the image quality (Score standard: Good is 4.0 and excellent is 5.0) of SQNet on HC/AMI with the segmentation score 4.60/4.58 and the T2 quantification score 4.32/4.42, which is higher than the compared state-of-the-art segmentation score (4.50/4.44) and the T2 quantification score (3.59/4.37). Thus, SQNet provides an accurate simultaneous segmentation and quantification, providing the potentially more accurate diagnosis of cardiac disease, such as AMI. Copyright © 2024, T

关键词： Mapping

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Personalized Heart Disease Detection via ECG Digital Twin Generation

arXiv

引用

arXiv 2024年

作者： Hu, Yaojun Chen, Jintai Hu, Lianting Li, Dantong Yan, Jiahuan Ying, Haochao Liang, Huiying Wu, Jian College of Computer Science and Technology Zhejiang University China Computer Science Department University of Illinois at Urbana-Champaign United States Medical Big Data Center Guangdong Provincial People’s Hospital Southern Medical University China Guangdong Cardiovascular Institute Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences China School of Public Health Zhejiang University China State Key Laboratory of Transvascular Implantation Devices of The Second Affiliated Hospital Zhejiang University School of Medicine China

Heart diseases rank among the leading causes of global mortality, demonstrating a crucial need for early diagnosis and intervention. Most traditional electrocardiogram (ECG) based automated diagnosis methods are trained at population level, neglecting the customization of personalized ECGs to enhance individual healthcare management. A potential solution to address this limitation is to employ digital twins to simulate symptoms of diseases in real patients. In this paper, we present an innovative prospective learning approach for personalized heart disease detection, which generates digital twins of healthy individuals’ anomalous ECGs and enhances the model sensitivity to the personalized symptoms. In our approach, a vector quantized feature separator is proposed to locate and isolate the disease symptom and normal segments in ECG signals with ECG report guidance. Thus, the ECG digital twins can simulate specific heart diseases used to train a personalized heart disease detection model. Experiments demonstrate that our approach not only excels in generating high-fidelity ECG signals but also improves personalized heart disease detection. Moreover, our approach ensures robust privacy protection, safeguarding patient data in model development. The code can be found at https://***/huyjj/LAVQ-Editor. Copyright © 2024, The Authors. All rights reserved.

关键词： Electrocardiograms

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A Spectral Efficient Hybrid Precoding Algorithm for MmWave MIMO Systems

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Procedia Computer Science 2020年 174卷 584-590页

作者： Tingting Li Feng Zhao Key Laboratory of Cognitive Radio and Information Processing Guilin University of Electronic Technology Guilin 541004 China Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Yulin China

Millimeter-wave MIMO will use hybrid precoding to improve the spectral efficiency. In this paper, we propose a spectral efficient hybrid precoding algorithm. In order to maximize the projection of hybrid precoding matrix on the optimal precoding matrix, we design the analog precoding matrix by selecting an optimal combination from the candidate matrix. Then, we regard the channel matrix and the obtained analog precoding matrix as an equivalent channel matrix, and use regularized zero-forcing to acquire the digital precoding matrix. Finally, we present simulation results of the proposed hybrid precoding algorithm and show that the algorithm approaches the optimal precoding.

关键词： Millimeter wave MIMO spectral efficiency hybrid precoding candidate matrix equivalent channel matrix

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