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ADAPTIVE SHRINKAGE ESTIMATION FOR PERSONALIZED DEEP KERNEL REGRESSION IN MODELING BRaiN TRAJECTORIES

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arXiv 2025年

作者： Tassopoulou, Vasiliki Shou, Haochang Davatzikos, Christos Center for AI and Data Science for Integrated Diagnostics University of Pennsylvania United States Department of Bioengineering University of Pennsylvania United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania United States

Longitudinal biomedical studies monitor individuals over time to capture dynamics in brain development, disease progression, and treatment effects. However, estimating trajectories of brain biomarkers is challenging due to biological variability, inconsistencies in measurement protocols (e.g., differences in MRI scanners) as well as scarcity and irregularity in longitudinal measurements. Herein, we introduce a novel personalized deep kernel regression framework for forecasting brain biomarkers, with application to regional volumetric measurements. Our approach integrates two key components: a population model that captures brain trajectories from a large and diverse cohort, and a subject-specific model that captures individual trajectories. To optimally combine these, we propose Adaptive Shrinkage Estimation, which effectively balances population and subject-specific models. We assess our model’s performance through predictive accuracy metrics, uncertainty quantification, and validation against external clinical studies. Benchmarking against state-of-the-art statistical and machine learning models—including linear mixed effects models, generalized additive models, and deep learning methods—demonstrates the superior predictive performance of our approach. Additionally, we apply our method to predict trajectories of composite neuroimaging biomarkers, which highlights the versatility of our approach in modeling the progression of longitudinal neuroimaging biomarkers. Furthermore, validation on three external neuroimaging studies confirms the robustness of our method across different clinical contexts. We make the code available at https://***/vatass/AdaptiveShrinkageDKGP. © 2025, CC BY.

关键词： Aerobraking

Single-cell transcriptomics and epigenomics point to CD58-CD2 interaction in controlling primary melanoma growth and immunity

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Cancer Communications 2025年第4期45卷 465-470页

作者： Antonia Stubenvoll Maria Schmidt Johanna Moeller Max Alexander Lingner Chango Carolyn Schultz Olga Antoniadou Henry Loeffler-Wirth Stephan Bernhart Florian Groβe Beatrice Thier Annette Paschen Ulf Anderegg Jan CSimon Mirjana Ziemer Clara TSchoeder Hans Binder Manfred Kunz Department of Dermatology Venereology and AllergologyUniversity of Leipzig Medical CenterLeipzigGermany Interdisciplinary Center for Bioinformatics University of LeipzigLeipzigGermany Institute for Drug Discovery University of Leipzig Medical CenterLeipzigGermany Bioinformatics Group Department of Computer ScienceUniversity of LeipzigLeipzigGermany Department of Diagnostics Fraunhofer Institute of Cell Therapy and Immunology(IZI)LeipzigGermany Center for Scalable Data Analytics and Artificial Intelligence(ScaDS.AI)Dresden/Leipzig LeipzigGermany Department of Dermatology University Hospital EssenUniversity Duisburg-Essen and German Cancer Consortium(DKTK)Partner Site Essen/DüsseldorfEssenGermany

Immunotherapy is currently one of the most promising treatment options for malignant melanoma[1].To uncover new immunological targets for future treatment approaches,single-cell transcriptomic and epigenomic analyses ... 详细信息

关键词： malignant melanoma single cell transcriptomics melanocytic nevus immunity immunotherapy primary melanoma epigenomic analyses epigenomics cd cd interaction

Formation of a Single Bioconvection Spot in Euglena Suspension Induced by Negative Phototaxis

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arXiv 2025年

作者： Yamashita, Hiroshi Yamaguchi, Takayuki Suematsu, Nobuhiko J. Izumi, Shunsuke Iima, Makoto Graduate School of Integrated Sciences for Life Hiroshima University Hiroshima Japan Data Science and AI Innovation Research Promotion Center Shiga University Shiga Japan Graduate School of Advanced Mathematical Sciences Meiji University Tokyo Japan

Microorganisms are known to alter their motility in response to external stimuli. A typical example is the response known as taxis, which includes behaviors such as moving toward a light source (positive phototaxis) or away from it (negative phototaxis). In this study, we focused on bioconvection induced by the negative phototaxis of a Euglena suspension exposed to strong light from the bottom. Recent studies have revealed that Euglena bioconvection induced by negative phototaxis exhibits localized structure. In a previous study, we found that a single Euglena bioconvection spot (EBC spot) maintains its structure in a cylindrical container. Such a localized structure is not exclusive to Euglena, but has also reported in red tide algae, suggesting that this is a universal feature of bioconvection phenomena. In this paper, we investigated the formation conditions of EBC spot phenomena through experiments and numerical simulations. In the experiments, we examined the variation in EBC spot formation as a function of suspension height and average cell density, revealing that the critical average cell density increases with suspension height. In the numerical simulations, we conducted bioconvection simulations based on a model of Euglena motility that incorporated three types of behavior: the negative gravitaxis, negative phototaxis, and behavior of moving toward darker areas. Our model successfully reproduced the localized state of a two-dimensional EBC spot. Furthermore, calculations corresponding to the experimental conditions of suspension height and average cell density revealed that the critical average cell density for bioconvection decreases as the suspension height increases. Copyright © 2025, The Authors. All rights reserved.

关键词： Cells

Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation

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arXiv 2023年

作者： Gu, Zhouhong Zhu, Xiaoxuan Ye, Haoning Zhang, Lin Wang, Jianchen Zhu, Yixin Jiang, Sihang Xiong, Zhuozhi Li, Zihan Wu, Weijie He, Qianyu Xu, Rui Huang, Wenhao Liu, Jingping Wang, Zili Wang, Shusen Zheng, Weiguo Feng, Hongwei Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China School of Information Science and Engineering East China University of Science and Technology China School of Data Science Fudan University China Fudan-Aishu Cognitive Intelligence Joint Research Center China Research Group of Computational and AI Communication Institute for Global Communications and Integrated Media Fudan University China

New Natural Langauge Process (NLP) benchmarks are urgently needed to align with the rapid development of large language models (LLMs). We present Xiezhi, the most comprehensive evaluation suite designed to assess holistic domain knowledge. Xiezhi comprises multiple-choice questions across 516 diverse disciplines ranging from 13 different subjects with 249,587 questions and accompanied by Xiezhi-Specialty with 14,041 questions and Xiezhi-Interdiscipline with 10,746 questions. We conduct evaluation of the 47 cutting-edge LLMs on Xiezhi. Results indicate that LLMs exceed average performance of humans in science, engineering, agronomy, medicine, and art, but fall short in economics, jurisprudence, pedagogy, literature, history, and management. © 2023, CC BY.

关键词： Domain Knowledge

Neuroanatomical Heterogeneity and Similarities Across Nine ai Dimensions in Four Brain Disorders in the General Population

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Biological Psychiatry 2025年第9期97卷 S40-S40页

作者： Filippos Anagnostakis Christos Davatzikos Junhao Wen Laboratory of AI and Biomedical Science (LABS) University of Southern California Los Angeles Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for AI and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Columbia University

Adapting Machine Learning Diagnostic Models to New Populations Using a Small Amount of data: Results from Clinical Neuroscience

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arXiv 2023年

作者： Wang, Rongguang Erus, Guray Chaudhari, Pratik Davatzikos, Christos Department of Electrical and Systems Engineering University of Pennsylvania United States Center for AI and Data Science for Integrated Diagnostics University of Pennsylvania United States Center for Biomedical Image Computing and Analytics University of Pennsylvania United States Department of Computer and Information Science University of Pennsylvania United States Department of Radiology Perelman School of Medicine University of Pennsylvania United States Center for Biomedical Image Computing and Analytics University of Pennsylvania 3700 Hamilton Walk 7th Floor PhiladelphiaPA19104 United States

Machine learning (ML) is revolutionizing many areas of engineering and science, including healthcare. However, it is also facing a reproducibility crisis, especially in healthcare. ML models that are carefully constructed from and evaluated on data from one part of the population may not generalize well on data from a different population group, or acquisition instrument settings and acquisition protocols. We tackle this problem in the context of neuroimaging of Alzheimer's disease (AD), schizophrenia (SZ) and brain aging. We develop a weighted empirical risk minimization approach that optimally combines data from a source group, e.g., subjects are stratified by attributes such as sex, age group, race and clinical cohort to make predictions on a target group, e.g., other sex, age group, etc. using a small fraction (10%) of data from the target group. We apply this method to multi-source data of 15,363 individuals from 20 neuroimaging studies to build ML models for diagnosis of AD and SZ, and estimation of brain age. We found that this approach achieves substantially better accuracy than existing domain adaptation techniques: it obtains area under curve greater than 0.95 for AD classification, area under curve greater than 0.7 for SZ classification and mean absolute error less than 5 years for brain age prediction on all target groups, achieving robustness to variations of scanners, protocols, and demographic or clinical characteristics. In some cases, it is even better than training on all data from the target group, because it leverages the diversity and size of a larger training set. We also demonstrate the utility of our models for prognostic tasks such as predicting disease progression in individuals with mild cognitive impairment. Critically, our brain age prediction models lead to new clinical insights regarding correlations with neurophysiological tests. In summary, we present a relatively simple methodology, along with ample experimental evidence, supporting

关键词： Neuroimaging

Bias in Machine Learning Models Can Be Significantly Mitigated by Careful Training: Evidence from Neuroimaging Studies

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arXiv 2022年

作者： Wang, Rongguang Chaudhari, Pratik Davatzikos, Christos Department of Electrical and Systems Engineering University of Pennsylvania United States Center for AI and Data Science for Integrated Diagnostics University of Pennsylvania United States Center for Biomedical Image Computing and Analytics University of Pennsylvania United States Department of Computer and Information Science University of Pennsylvania United States Department of Radiology Perelman School of Medicine University of Pennsylvania United States

Despite the great promise that machine learning has offered in many fields of medicine, it has also raised concerns about potential biases and poor generalization across genders, age distributions, races and ethnicities, hospitals, and data acquisition equipment and protocols. In the current study, and in the context of three brain diseases, we provide evidence which suggests that when properly trained, machine learning models can generalize well across diverse conditions and do not necessarily suffer from bias. Specifically, by using multi-study magnetic resonance imaging consortia for diagnosing Alzheimer's disease, schizophrenia, and autism spectrum disorder, we find that well-trained models have a high area-under-the-curve (AUC) on subjects across different subgroups pertaining to attributes such as gender, age, racial groups, and different clinical studies and are unbiased under multiple fairness metrics such as demographic parity difference, equalized odds difference, equal opportunity difference etc. We find that models that incorporate multi-source data from demographic, clinical, genetic factors and cognitive scores are also unbiased. These models have better predictive AUC across subgroups than those trained only with imaging features but there are also situations when these additional features do not help. © 2022, CC BY.

关键词： Magnetic resonance imaging

Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience

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arXiv 2022年

作者： Wang, Rongguang Bashyam, Vishnu Yang, Zhijian Yu, Fanyang Tassopoulou, Vasiliki Chintapalli, Sai Spandana Skampardoni, Ioanna Sreepada, Lasya P. Sahoo, Dushyant Nikita, Konstantina Abdulkadir, Ahmed Wen, Junhao Davatzikos, Christos Center for AI and Data Science for Integrated Diagnostics University of Pennsylvania Philadelphia United States Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia United States School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Department of Clinical Neurosciences Lausanne University Hospital University of Lausanne Lausanne Switzerland Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia United States

Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to the broader family of generative methods, which learn to generate realistic data with a probabilistic model by learning distributions from real samples. In the clinical context, GANs have shown enhanced capabilities in capturing spatially complex, nonlinear, and potentially subtle disease effects compared to traditional generative methods. This review critically appraises the existing literature on the applications of GANs in imaging studies of various neurological conditions, including Alzheimer’s disease, brain tumors, brain aging, and multiple sclerosis. We provide an intuitive explanation of various GAN methods for each application and further discuss the main challenges, open questions, and promising future directions of leveraging GANs in neuroimaging. We aim to bridge the gap between advanced deep learning methods and neurology research by highlighting how GANs can be leveraged to support clinical decision making and contribute to a better understanding of the structural and functional patterns of brain diseases. © 2022, CC BY.

关键词： Generative adversarial networks

Neuromodulated Meta-Learning

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arXiv 2024年

作者： Wang, Jingyao Guo, Huijie Qiang, Wenwen Li, Jiangmeng Zheng, Changwen Xiong, Hui Hua, Gang University of Chinese Academy of Sciences Beijing China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Thrust of Artificial Intelligence Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Wormpex AI Research LLC BellevueWA98004 United States

Humans excel at adapting perceptions and actions to diverse environments, enabling efficient interaction with the external world. This adaptive capability relies on the biological nervous system (BNS), which activates different brain regions for distinct tasks. Meta-learning similarly trains machines to handle multiple tasks but relies on a fixed network structure, not as flexible as BNS. To investigate the role of flexible network structure (FNS) in meta-learning, we conduct extensive empirical and theoretical analyses, finding that model performance is tied to structure, with no universally optimal pattern across tasks. This reveals the crucial role of FNS in meta-learning, ensuring meta-learning to generate the optimal structure for each task, thereby maximizing the performance and learning efficiency of meta-learning. Motivated by this insight, we propose to define, measure, and model FNS in meta-learning. First, we define that an effective FNS should possess frugality, plasticity, and sensitivity. Then, to quantify FNS in practice, we present three measurements for these properties, collectively forming the structure constraint with theoretical supports. Building on this, we finally propose Neuromodulated Meta-Learning (NeuronML) to model FNS in meta-learning. It utilizes bi-level optimization to update both weights and structure with the structure constraint. Extensive theoretical and empirical evaluations demonstrate the effectiveness of NeuronML on various tasks. Code is publicly available at https://***/WangJingyao07/NeuronML. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning