ArXiv Pub Date : 2024-11-13

Qiqi Xian, Zhe Sage Chen

{"title":"Inferring directed spectral information flow between mixed-frequency time series.","authors":"Qiqi Xian, Zhe Sage Chen","doi":"","DOIUrl":"","url":null,"abstract":"Identifying directed spectral information flow between multivariate time series is important for many applications in finance, climate, geophysics and neuroscience. Spectral Granger causality (SGC) is a prediction-based measure characterizing directed information flow at specific oscillatory frequencies. However, traditional vector autoregressive (VAR) approaches are insufficient to assess SGC when time series have mixed frequencies (MF) or are coupled by nonlinearity. Here we propose a time-frequency canonical correlation analysis approach (\"MF-TFCCA\") to assess the strength and driving frequency of spectral information flow. We validate the approach with extensive computer simulations on MF time series under various interaction conditions and further assess statistical significance of the estimate with surrogate data. In various benchmark comparisons, MF-TFCCA consistently outperforms the traditional parametric MF-VAR model in both computational efficiency and detection accuracy, and recovers the dominant driving frequencies. We further apply MF-TFCCA to real-life finance, climate and neuroscience data. Our analysis framework provides an exploratory and computationally efficient nonparametric approach to quantify directed information flow between MF time series in the presence of complex and nonlinear interactions.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11343236/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142057532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning collective cell migratory dynamics from a static snapshot with graph neural networks. 利用深度神经网络从多细胞图中学习动态。

ArXiv Pub Date : 2024-11-11

Haiqian Yang, Florian Meyer, Shaoxun Huang, Liu Yang, Cristiana Lungu, Monilola A Olayioye, Markus J Buehler, Ming Guo

引用次数: 0

The doctor will polygraph you now: ethical concerns with AI for fact-checking patients. 医生现在要对你进行测谎：人工智能对病人进行事实核查的伦理问题。

ArXiv Pub Date : 2024-11-11

James Anibal, Jasmine Gunkel, Shaheen Awan, Hannah Huth, Hang Nguyen, Tram Le, Jean-Christophe Bélisle-Pipon, Micah Boyer, Lindsey Hazen, Yael Bensoussan, David Clifton, Bradford Wood

{"title":"The doctor will polygraph you now: ethical concerns with AI for fact-checking patients.","authors":"James Anibal, Jasmine Gunkel, Shaheen Awan, Hannah Huth, Hang Nguyen, Tram Le, Jean-Christophe Bélisle-Pipon, Micah Boyer, Lindsey Hazen, Yael Bensoussan, David Clifton, Bradford Wood","doi":"","DOIUrl":"","url":null,"abstract":"Artificial intelligence (AI) methods have been proposed for the prediction of social behaviors which could be reasonably understood from patient-reported information. This raises novel ethical concerns about respect, privacy, and control over patient data. Ethical concerns surrounding clinical AI systems for social behavior verification can be divided into two main categories: (1) the potential for inaccuracies/biases within such systems, and (2) the impact on trust in patient-provider relationships with the introduction of automated AI systems for \"fact-checking\", particularly in cases where the data/models may contradict the patient. Additionally, this report simulated the misuse of a verification system using patient voice samples and identified a potential LLM bias against patient-reported information in favor of multi-dimensional data and the outputs of other AI methods (i.e., \"AI self-trust\"). Finally, recommendations were presented for mitigating the risk that AI verification methods will cause harm to patients or undermine the purpose of the healthcare system.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11468487/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BrainSegFounder: Towards 3D Foundation Models for Neuroimage Segmentation. BrainFounder：为神经图像分析建立大脑基础模型。

ArXiv Pub Date : 2024-11-07

Joseph Cox, Peng Liu, Skylar E Stolte, Yunchao Yang, Kang Liu, Kyle B See, Huiwen Ju, Ruogu Fang

{"title":"BrainSegFounder: Towards 3D Foundation Models for Neuroimage Segmentation.","authors":"Joseph Cox, Peng Liu, Skylar E Stolte, Yunchao Yang, Kang Liu, Kyle B See, Huiwen Ju, Ruogu Fang","doi":"","DOIUrl":"","url":null,"abstract":"The burgeoning field of brain health research increasingly leverages artificial intelligence (AI) to interpret and analyze neurological data. This study introduces a novel approach towards the creation of medical foundation models by integrating a large-scale multi-modal magnetic resonance imaging (MRI) dataset derived from 41,400 participants in its own. Our method involves a novel two-stage pretraining approach using vision transformers. The first stage is dedicated to encoding anatomical structures in generally healthy brains, identifying key features such as shapes and sizes of different brain regions. The second stage concentrates on spatial information, encompassing aspects like location and the relative positioning of brain structures. We rigorously evaluate our model, BrainFounder, using the Brain Tumor Segmentation (BraTS) challenge and Anatomical Tracings of Lesions After Stroke v2.0 (ATLAS v2.0) datasets. BrainFounder demonstrates a significant performance gain, surpassing the achievements of the previous winning solutions using fully supervised learning. Our findings underscore the impact of scaling up both the complexity of the model and the volume of unlabeled training data derived from generally healthy brains, which enhances the accuracy and predictive capabilities of the model in complex neuroimaging tasks with MRI. The implications of this research provide transformative insights and practical applications in healthcare and make substantial steps towards the creation of foundation models for Medical AI. Our pretrained models and training code can be found at https://github.com/lab-smile/GatorBrain.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11343227/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142057529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Learnable Prior Improves Inverse Tumor Growth Modeling. 可学习的先验改进了肿瘤逆生长建模

ArXiv Pub Date : 2024-11-06

Jonas Weidner, Ivan Ezhov, Michal Balcerak, Marie-Christin Metz, Sergey Litvinov, Sebastian Kaltenbach, Leonhard Feiner, Laurin Lux, Florian Kofler, Jana Lipkova, Jonas Latz, Daniel Rueckert, Bjoern Menze, Benedikt Wiestler

{"title":"A Learnable Prior Improves Inverse Tumor Growth Modeling.","authors":"Jonas Weidner, Ivan Ezhov, Michal Balcerak, Marie-Christin Metz, Sergey Litvinov, Sebastian Kaltenbach, Leonhard Feiner, Laurin Lux, Florian Kofler, Jana Lipkova, Jonas Latz, Daniel Rueckert, Bjoern Menze, Benedikt Wiestler","doi":"","DOIUrl":"","url":null,"abstract":"Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a substantial challenge, either due to the high computational requirements of model-based approaches or the limited robustness of deep learning (DL) methods. We propose a novel framework that leverages the unique strengths of both approaches in a synergistic manner. Our method incorporates a DL ensemble for initial parameter estimation, facilitating efficient downstream evolutionary sampling initialized with this DL-based prior. We showcase the effectiveness of integrating a rapid deep-learning algorithm with a high-precision evolution strategy in estimating brain tumor cell concentrations from magnetic resonance images. The DL-Prior plays a pivotal role, significantly constraining the effective sampling-parameter space. This reduction results in a fivefold convergence acceleration and a Dice-score of 95%.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10942480/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140144757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transcription factor clusters as information transfer agents. 作为信息传递媒介的转录因子群。

ArXiv Pub Date : 2024-11-06

Rahul Munshi, Jia Ling, Sergey Ryabichko, Eric Wieschaus, Thomas Gregor

引用次数: 0

Back to the Continuous Attractor. 回到 "持续吸引器"。

ArXiv Pub Date : 2024-11-05

Ábel Ságodi, Guillermo Martín-Sánchez, Piotr Sokół, Il Memming Park

{"title":"Back to the Continuous Attractor.","authors":"Ábel Ságodi, Guillermo Martín-Sánchez, Piotr Sokół, Il Memming Park","doi":"","DOIUrl":"","url":null,"abstract":"Continuous attractors offer a unique class of solutions for storing continuous-valued variables in recurrent system states for indefinitely long time intervals. Unfortunately, continuous attractors suffer from severe structural instability in general--they are destroyed by most infinitesimal changes of the dynamical law that defines them. This fragility limits their utility especially in biological systems as their recurrent dynamics are subject to constant perturbations. We observe that the bifurcations from continuous attractors in theoretical neuroscience models display various structurally stable forms. Although their asymptotic behaviors to maintain memory are categorically distinct, their finite-time behaviors are similar. We build on the persistent manifold theory to explain the commonalities between bifurcations from and approximations of continuous attractors. Fast-slow decomposition analysis uncovers the persistent manifold that survives the seemingly destructive bifurcation. Moreover, recurrent neural networks trained on analog memory tasks display approximate continuous attractors with predicted slow manifold structures. Therefore, continuous attractors are functionally robust and remain useful as a universal analogy for understanding analog memory.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11312635/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141918312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modular control of Boolean network models. 生物网络的模块化控制

ArXiv Pub Date : 2024-11-04

David Murrugarra, Alan Veliz-Cuba, Elena Dimitrova, Claus Kadelka, Matthew Wheeler, Reinhard Laubenbacher

{"title":"Modular control of Boolean network models.","authors":"David Murrugarra, Alan Veliz-Cuba, Elena Dimitrova, Claus Kadelka, Matthew Wheeler, Reinhard Laubenbacher","doi":"","DOIUrl":"","url":null,"abstract":"The concept of control is crucial for effectively understanding and applying biological network models. Key structural features relate to control functions through gene regulation, signaling, or metabolic mechanisms, and computational models need to encode these. Applications often focus on model-based control, such as in biomedicine or metabolic engineering. In a recent paper, the authors developed a theoretical framework of modularity in Boolean networks, which lead to a canonical semidirect product decomposition of these systems. In this paper, we present an approach to model-based control that exploits this modular structure, as well as the canalizing features of the regulatory mechanisms. We show how to identify control strategies from the individual modules, and we present a criterion based on canalizing features of the regulatory rules to identify modules that do not contribute to network control and can be excluded. For even moderately sized networks, finding global control inputs is computationally challenging. Our modular approach leads to an efficient approach to solving this problem. We apply it to a published Boolean network model of blood cancer large granular lymphocyte (T-LGL) leukemia to identify a minimal control set that achieves a desired control objective.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10854280/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139725322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Augmenting Biomedical Named Entity Recognition with General-domain Resources. 利用通用领域资源增强生物医学命名实体识别。

ArXiv Pub Date : 2024-11-03

Yu Yin, Hyunjae Kim, Xiao Xiao, Chih Hsuan Wei, Jaewoo Kang, Zhiyong Lu, Hua Xu, Meng Fang, Qingyu Chen

{"title":"Augmenting Biomedical Named Entity Recognition with General-domain Resources.","authors":"Yu Yin, Hyunjae Kim, Xiao Xiao, Chih Hsuan Wei, Jaewoo Kang, Zhiyong Lu, Hua Xu, Meng Fang, Qingyu Chen","doi":"","DOIUrl":"","url":null,"abstract":"Training a neural network-based biomedical named entity recognition (BioNER) model usually requires extensive and costly human annotations. While several studies have employed multi-task learning with multiple BioNER datasets to reduce human effort, this approach does not consistently yield performance improvements and may introduce label ambiguity in different biomedical corpora. We aim to tackle those challenges through transfer learning from easily accessible resources with fewer concept overlaps with biomedical datasets. In this paper, we proposed GERBERA, a simple-yet-effective method that utilized a general-domain NER dataset for training. Specifically, we performed multi-task learning to train a pre-trained biomedical language model with both the target BioNER dataset and the general-domain dataset. Subsequently, we fine-tuned the models specifically for the BioNER dataset. We systematically evaluated GERBERA on five datasets of eight entity types, collectively consisting of 81,410 instances. Despite using fewer biomedical resources, our models demonstrated superior performance compared to baseline models trained with multiple additional BioNER datasets. Specifically, our models consistently outperformed the baselines in six out of eight entity types, achieving an average improvement of 0.9% over the best baseline performance across eight biomedical entity types sourced from five different corpora. Our method was especially effective in amplifying performance on BioNER datasets characterized by limited data, with a 4.7% improvement in F1 scores on the JNLPBA-RNA dataset.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11468657/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Forecasting Treatment Response with Deep Pharmacokinetic Encoders. 用全局深度学习和患者特异性药代动力学先验预测治疗反应。

ArXiv Pub Date : 2024-11-02

Willa Potosnak, Cristian Challu, Kin Gutierrez Olivares, Keith Dufendach, Artur Dubrawski

引用次数: 0

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