Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery
Central South University（中南大学）；Xinjiang University（新疆大学）；Shenzhen University of Advanced Technology（深圳先进技术研究院？不，正确翻译：深圳理工大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Dundee（邓迪大学）；Griffith University（格里菲斯大学）；Shenzhen Health Development Research and Data Management Center（深圳卫生发展研究与数据管理中心）
Jin Liu, Junbin Mao, Hanhe Lin, Hulin Kuang, Shirui Pan, Xusheng Wu, Shan Xie, Fei Liu, Yi Pan
由于其复杂性，基于图学习的多模态整合与分类是疾病预测中最具挑战性的障碍之一。为了有效抵消多模态整合过程中模态间的负面影响，并从图中提取异质信息，我们提出了一种名为MMKGL（多模态多核图学习）的新方法。针对模态间的负面影响问题，我们提出了一个多模态图嵌入模块来构建多模态图。与为所有模态手动构建静态图的传统方法不同，每个模态通过自适应学习生成单独的图，其中在多图融合嵌入过程中引入函数图和监督图进行优化。然后，我们提出一个多核图学习模块，从多模态图中提取异质信息。多模态图中不同层次的信息通过不同感受野大小的卷积核进行聚合，随后生成跨核发现张量用于疾病预测。我们的方法在基准数据集Autism Brain Imaging Data Exchange（ABIDE）上进行了评估，性能优于当前最先进的方法。此外，我们的模型识别出与自闭症相关的具有鉴别性的脑区，为自闭症病理学研究提供了指导。
英文摘要：
Due to its complexity, graph learning-based multi-modal integration and classification is one of the most challenging obstacles for disease prediction. To effectively offset the negative impact between modalities in the process of multi-modal integration and extract heterogeneous information from graphs, we propose a novel method called MMKGL (Multi-modal Multi-Kernel Graph Learning). For the problem of negative impact between 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.
Autism spectrum disorder classification based on interpersonal neural synchrony: Can classification be improved by dyadic neural biomarkers using unsupervised graph representation learning?
JARA-Brain Institute II（JARA脑科学第二研究所）；RWTH Aachen University（亚琛工业大学）；Research Centre Juelich（于利希研究中心）；University Medical Center Göttingen（哥廷根大学医学中心）；Leibniz ScienceCampus Primate Cognition（莱布尼茨灵长类认知科学园区）
Network-Based Detection of Autism Spectrum Disorder Using Sustainable and Non-invasive Salivary Biomarkers
Faculty of Computing, Federal University of Uberlândia（计算学院，伯南迪利亚联邦大学）；Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlândia（生理学系，生物医学科学研究所，伯南迪利亚联邦大学）
Discovering robust biomarkers of psychiatric disorders from resting-state functional MRI via graph neural networks: A systematic review
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Department of Computer Science and Engineering, Indian Institute of Technology（印度理工学院计算机科学与工程系）
Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction
Tulane University（杜兰大学）；Xavier University of Louisiana（路易斯安那泽维尔大学）；Georgia State University（佐治亚州立大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）
Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers
Department of Encephalopathy, Chengdu Pidu District Hospital of Traditional Chinese Medicine（脑病科，成都_pidu区中医医院）；Department of Tuina, Chengdu Pidu District Hospital of Traditional Chinese Medicine（推拿科，成都_pidu区中医医院）；Wuhan Hospital of Integrated Traditional Chinese and Western Medicine, Affiliated to Hubei University of Chinese Medicine（中西医结合医院，湖北中医药大学附属医院）；Department of Electrical and Computer Engineering, Carnegie Mellon University（电气与计算机工程系，卡内基梅隆大学）
Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review
Islamic Azad University（伊斯兰阿扎德大学）；Ferdowsi University of Mashhad（马什哈德菲尔多西大学）；Semnan University（塞姆南大学）；Qatar Biomedical Research Institute（卡塔尔生物医学研究所）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）；University of Granada（格拉纳达大学）；Deakin University（迪肯大学）；University of Technology Sydney（悉尼科技大学）；University of Turku（图尔库大学）；Effat University（埃法特大学）；Ngee Ann Polytechnic（义安理工学院）；Asia University（亚洲大学）；Singapore University of Social Sciences（新加坡社会科学大学）
CALM: Interpretable Cross-Modal Alignment for Biomarker Discovery from Unpaired Data
Department of Electrical and Computer Engineering, Boston University（波士顿大学电气与计算机工程系）；School of Data Science, University of Virginia（弗吉尼亚大学数据科学学院）；Department of Psychology, University of Virginia（弗吉尼亚大学心理学系）；Department of Neurology, University of Virginia（弗吉尼亚大学神经病学系）；Departments of Computer Science and Biology, Johns Hopkins University（约翰霍普金斯大学计算机科学与生物学系）
G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification
Johns Hopkins University（约翰斯·霍普金斯大学）；Lieber Institute for Brain Development（利伯大脑发育研究所）；University of Bari Aldo Moro（巴里大学阿尔多·莫罗大学）；Azienda Ospedaliero-Universitaria Consorziale Policlinico（巴里大学联合综合医院）
OPTIMUS: Predicting Multivariate Outcomes in Alzheimer's Disease Using Multi-modal Data amidst Missing Values
University Lausanne Hospital (CHUV)（洛桑大学医院）；University of Lausanne (UNIL)（洛桑大学）；Zhejiang University（浙江大学）；Zhejiang University School of Medicine（浙江大学医学院）
Robust Multi-Omics Integration from Incomplete Modalities Significantly Improves Prediction of Alzheimer's Disease
LG AI Research, Seoul, Republic of Korea（LG AI研究院，韩国首尔）；Department of Chemistry, Seoul National University, Seoul, Republic of Korea（首尔国立大学化学系）；The Jackson Laboratory, Bar Harbor, Maine, USA（杰克逊实验室，缅因州巴尔 Harbor）
Linking spatial biology and clinical histology via Haiku
Department of Pathology and Laboratory Medicine, University of Pennsylvania（宾夕法尼亚大学病理学与实验室医学系）；Department of Bioengineering, University of Pennsylvania（宾夕法尼亚大学生物工程系）；Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania（宾夕法尼亚大学生物统计学、流行病学与信息学系）；Enable Medicine, Menlo Park, CA, USA（Enable Medicine）
Advancing Digital Precision Medicine for Chronic Fatigue Syndrome through Longitudinal Large-Scale Multi-Modal Biological Omics Modeling with Machine Learning and Artificial Intelligence
