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IEEE Transactions on Computational Biology and bioinformatics 2025年第2期22卷 842-854页

作者： Jin Liu Junbin Mao Hanhe Lin Hulin Kuang Shirui Pan Xusheng Wu Shan Xie Fei Liu Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi China Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China School of Science and Engineering University of Dundee Dundee U.K. School of Information and Communication Technology Griffith University Gold Coast QLD Australia Shenzhen Health Development Research and Data Management Center Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Graph learning-based multi-modal integration and classification is one of the most challenging tasks for disease prediction. To effectively offset the negative impact among modalities in the process of multi-modal integration and heterogeneous information extractions from graphs, we propose a novel method called Multi-modal Multi-Kernel Graph Learning (MMKGL). To solve the problem of negative impact among modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging Data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology.

关键词： Diseases Autism Random access memory Data mining Kernel Electronic mail Training Neuroimaging Graph convolutional networks bioinformatics

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MTFIL-Net: Automated Alzheimer's disease detection and MMSE score prediction based on feature interactive learning

MTFIL-Net: Automated Alzheimer's disease detection and MMSE ...

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2021 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2021

作者： Liu, Jin Tian, Xu Wang, Jianxin Guo, Rui Kuang, Hulin Central South University Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Changsha China

ISBN: (纸本)9781665401265

Automatic detection of Alzheimer's disease (AD) is conducive to intervention in the disease progression. MMSE score prediction can reveal the development of AD. In recent years, some studies have designed multi-task methods for AD detection and MMSE score prediction to take advantage of the correlation between them. However, how to use the correlation between the two task features is still a problem. To address this challenge, we propose a multi-task feature interactive leanrning network (MTFIL-Net) to perform AD detection and MMSE score prediction. First, we interact the features acquired by CNNs corresponding to the two tasks to take advantage of the feature correlation between the two tasks. The interaction module extracts the shared features of the two tasks and concatenate them with the features of the two task. Then, we design a joint loss based on cross entropy and smooth L1 function. We use the distribution of MMSE scores to dynamically adjust the relationship between the two tasks. We validate our method with subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We use the ADNI1 dataset for training and testing, and used the ADNI2 dataset as an external validation set. Our proposed MTFIL-Net reached an ACC of 0.86 for AD detection and a correlation coefficient of 0.67 for MMSE score prediction on the ADNI1 dataset, and reached an ACC of 0.85 for AD detection and a correlation coefficient of 0.66 for MMSE score prediction on the ADNI2 dataset. Experiment results show that MTFIL-Net effectively utilizes the correlation between AD and MMSE score. © 2021 IEEE.

关键词： Forecasting

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MTFIL-Net: automated Alzheimer’s disease detection and MMSE score prediction based on feature interactive learning

MTFIL-Net: automated Alzheimer’s disease detection and MMSE...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Jin Liu Xu Tian Jianxin Wang Rui Guo Hulin Kuang Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9781665429825

Automatic detection of Alzheimer’s disease (AD) is conducive to intervention in the disease progression. MMSE score prediction can reveal the development of AD. In recent years, some studies have designed multi-task methods for AD detection and MMSE score prediction to take advantage of the correlation between them. However, how to use the correlation between the two task features is still a problem. To address this challenge, we propose a multi-task feature interactive leanrning network (MTFIL-Net) to perform AD detection and MMSE score prediction. First, we interact the features acquired by CNNs corresponding to the two tasks to take advantage of the feature correlation between the two tasks. The interaction module extracts the shared features of the two tasks and concatenate them with the features of the two task. Then, we design a joint loss based on cross entropy and smooth L1 function. We use the distribution of MMSE scores to dynamically adjust the relationship between the two tasks. We validate our method with subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We use the ADNI1 dataset for training and testing, and used the ADNI2 dataset as an external validation set. Our proposed MTFIL-Net reached an ACC of 0.86 for AD detection and a correlation coefficient of 0.67 for MMSE score prediction on the ADNI1 dataset, and reached an ACC of 0.85 for AD detection and a correlation coefficient of 0.66 for MMSE score prediction on the ADNI2 dataset. Experiment results show that MTFIL-Net effectively utilizes the correlation between AD and MMSE score.

关键词： Correlation coefficient Training Neuroimaging Correlation Feature extraction Multitasking Task analysis

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Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery

arXiv

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arXiv 2023年

作者： Liu, Jin Mao, Junbin Lin, Hanhe Kuang, Hulin Pan, Shirui Wu, Xusheng Xie, Shan Liu, Fei Pan, Yi Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830000 China Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Science and Engineering University of Dundee DundeeDD1 4HN United Kingdom School of Information and Communication Technology Griffith University Gold CoastQLD4215 Australia Shenzhen Health Development Research and Data Management Center Shenzhen518109 China

关键词： Graph embeddings

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Frontispiece: The Fourth Alloying Mode by Way of Anti-Galvanic Reaction

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Angewandte Chemie International Edition 2018年第17期57卷

作者： Dr. Min Zhu Dr. Pu Wang Nan Yan Xiaoqi Chai Lizhong He Yan Zhao Nan Xia Chuanhao Yao Prof. Dr. Jin Li Prof. Dr. Haiteng Deng Prof. Dr. Yan Zhu Prof. Dr. Yong Pei Prof. Dr. Zhikun Wu Key Laboratory of Materials Physics Anhui Key Laboratory of Nanomaterials and Nanotechnology CAS Center for Excellence in Nanoscience Institute of Solid State Physics Chinese Academy of Sciences Hefei 230031 China Institute of Physical Science and Information Technology Anhui University Hefei 230031 China University of Science and Technology of China Hefei 230026 China Department of Chemistry Key Laboratory of Environmentally Friendly Chemistry and Applications of Ministry of Education Xiangtan University Hunan Province Xiangtan 411105 China Key Lab of Mesoscopic Chemistry School of Chemistry and Chemical Engineering Nanjing University Nanjing 210093 China Tsinghua University-Peking University Joint Center for Life Sciences School of Life Sciences Tsinghua University Beijing 100084 China MOE Key Laboratory of Bioinformatics School of Life Sciences Tsinghua University Beijing 100084 China

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The Fourth Alloying Mode by Way of Anti‐Galvanic Reaction

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Angewandte Chemie 2018年第17期130卷

作者： Min Zhu Pu Wang Nan Yan Xiaoqi Chai Lizhong He Yan Zhao Nan Xia Chuanhao Yao Jin Li Haiteng Deng Yan Zhu Yong Pei Zhikun Wu Key Laboratory of Materials Physics Anhui Key Laboratory of Nanomaterials and Nanotechnology CAS Center for Excellence in Nanoscience Institute of Solid State Physics Chinese Academy of Sciences Hefei 230031 China Institute of Physical Science and Information Technology Anhui University Hefei 230031 China University of Science and Technology of China Hefei 230026 China Department of Chemistry Key Laboratory of Environmentally Friendly Chemistry and Applications of Ministry of Education Xiangtan University Hunan Province Xiangtan 411105 China Key Lab of Mesoscopic Chemistry School of Chemistry and Chemical Engineering Nanjing University Nanjing 210093 China Tsinghua University-Peking University Joint Center for Life Sciences School of Life Sciences Tsinghua University Beijing 100084 China MOE Key Laboratory of Bioinformatics School of Life Sciences Tsinghua University Beijing 100084 China

Anti‐galvanic reaction (AGR) not only defies classic galvanic theory but is a promising method for tuning the compositions, structures, and properties of noble‐metal nanoparticles. Employing AGR for the preparation of alloy nanoparticles has recently received great interest. Herein, we report an unprecedented alloying mode by way of AGR, in which foreign atoms induce structural transformation of the mother nanoparticles and enter the nanoparticles in a non‐replacement fashion. A novel, active‐metal‐doped, gold nanoparticle was synthesized by this alloying mode, and its structure resolved. A CdSH motif was found in the protecting staples of the bimetal nanoparticle. DFT calculations revealed that the Au 20 Cd 4 (SH)(SR) 19 nanoparticle is a 8e superatom cluster. Furthermore, although the Cd‐doping does not essentially alter the absorption spectrum of the mother nanocluster, it distinctly enhances the stability and catalytic selectivity of the mother nanoclusters.

关键词： Antigalvanische Reaktion (AGR) Gold Kristallstrukturen Legierungen Nanopartikel

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Frontispiz: The Fourth Alloying Mode by Way of Anti‐Galvanic Reaction

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Angewandte Chemie 2018年第17期130卷

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