ArXiv最新文献 - Book学术

BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool. BioNeuralNet：基于图神经网络的多组学网络数据分析工具。

ArXiv Pub Date : 2025-07-27

Vicente Ramos, Sundous Hussein, Mohamed Abdel-Hafiz, Arunangshu Sarkar, Weixuan Liu, Katerina J Kechris, Russell P Bowler, Leslie Lange, Farnoush Banaei-Kashani

{"title":"BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool.","authors":"Vicente Ramos, Sundous Hussein, Mohamed Abdel-Hafiz, Arunangshu Sarkar, Weixuan Liu, Katerina J Kechris, Russell P Bowler, Leslie Lange, Farnoush Banaei-Kashani","doi":"","DOIUrl":"","url":null,"abstract":"Multi-omics data offer unprecedented insights into complex biological systems, yet their high dimensionality, sparsity, and intricate interactions pose significant analytical challenges. Network-based approaches have advanced multi-omics research by effectively capturing biologically relevant relationships among molecular entities. While these methods are powerful for representing molecular interactions, there remains a need for tools specifically designed to effectively utilize these network representations across diverse downstream analyses. To fulfill this need, we introduce BioNeuralNet, a flexible and modular Python framework tailored for end-to-end network-based multi-omics data analysis. BioNeuralNet leverages Graph Neural Networks (GNNs) to learn biologically meaningful low-dimensional representations from multi-omics networks, converting these complex molecular networks into versatile embeddings. BioNeuralNet supports all major stages of multi-omics network analysis, including several network construction techniques, generation of low-dimensional representations, and a broad range of downstream analytical tasks. Its extensive utilities, including diverse GNN architectures, and compatibility with established Python packages (e.g., scikit-learn, PyTorch, NetworkX), enhance usability and facilitate quick adoption. BioNeuralNet is an open-source, user-friendly, and extensively documented framework designed to support flexible and reproducible multi-omics network analysis in precision medicine.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12310135/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144755357","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

Comprehensive characterization of nonlinear viscoelastic properties of arterial tissues using guided-wave optical coherence elastography. 用导波光学相干弹性成像综合表征动脉组织的非线性粘弹性特性。

ArXiv Pub Date : 2025-07-27

Yuxuan Jiang, Guo-Yang Li, Ruizhi Wang, Xu Feng, Yanhang Zhang, Seok-Hyun Yun

{"title":"Comprehensive characterization of nonlinear viscoelastic properties of arterial tissues using guided-wave optical coherence elastography.","authors":"Yuxuan Jiang, Guo-Yang Li, Ruizhi Wang, Xu Feng, Yanhang Zhang, Seok-Hyun Yun","doi":"","DOIUrl":"","url":null,"abstract":"The mechanical properties of arterial walls are critical for maintaining vascular function under pulsatile pressure and are closely linked to the development of cardiovascular diseases. Despite advances in imaging and elastography, comprehensive characterization of the complex mechanical behavior of arterial tissues remains challenging. Here, we present a broadband guided-wave optical coherence elastography (OCE) technique, grounded in viscoelasto-acoustic theory, for quantifying the nonlinear viscoelastic, anisotropic, and layer-specific properties of arterial walls with high spatial and temporal resolution. Our results reveal a strong stretch dependence of arterial viscoelasticity, with increasing prestress leading to a reduction in tissue viscosity. Under mechanical loading, the adventitia becomes significantly stiffer than the media, attributable to engagement of collagen fibers. Chemical degradation of collagen fibers highlighted their role in nonlinear viscoelasticity. This study demonstrates the potential of OCE as a powerful tool for detailed profiling of vascular biomechanics, with applications in basic research and future clinical diagnosis.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12310123/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144755359","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

Interpretable Graph Kolmogorov-Arnold Networks for Multi-Cancer Classification and Biomarker Identification using Multi-Omics Data. 利用多组学数据进行多种癌症分类和生物标志物鉴定的可解释图Kolmogorov-Arnold网络。

ArXiv Pub Date : 2025-07-27

Fadi Alharbi, Nishant Budhiraja, Aleksandar Vakanski, Boyu Zhang, Murtada K Elbashir, Harshith Guduru, Mohanad Mohammed

{"title":"Interpretable Graph Kolmogorov-Arnold Networks for Multi-Cancer Classification and Biomarker Identification using Multi-Omics Data.","authors":"Fadi Alharbi, Nishant Budhiraja, Aleksandar Vakanski, Boyu Zhang, Murtada K Elbashir, Harshith Guduru, Mohanad Mohammed","doi":"","DOIUrl":"","url":null,"abstract":"The integration of heterogeneous multi-omics datasets at a systems level remains a central challenge for developing analytical and computational models in precision cancer diagnostics. This paper introduces Multi-Omics Graph Kolmogorov-Arnold Network (MOGKAN), a deep learning framework that utilizes messenger-RNA, micro-RNA sequences, and DNA methylation samples together with Protein-Protein Interaction (PPI) networks for cancer classification across 31 different cancer types. The proposed approach combines differential gene expression with DESeq2, Linear Models for Microarray (LIMMA), and Least Absolute Shrinkage and Selection Operator (LASSO) regression to reduce multi-omics data dimensionality while preserving relevant biological features. The model architecture is based on the Kolmogorov-Arnold theorem principle and uses trainable univariate functions to enhance interpretability and feature analysis. MOGKAN achieves classification accuracy of 96.28 percent and exhibits low experimental variability in comparison to related deep learning-based models. The biomarkers identified by MOGKAN were validated as cancer-related markers through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. By integrating multi-omics data with graph-based deep learning, our proposed approach demonstrates robust predictive performance and interpretability with potential to enhance the translation of complex multi-omics data into clinically actionable cancer diagnostics.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12310127/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144755366","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

Predicting Neoadjuvant Chemotherapy Response in Triple-Negative Breast Cancer Using Pre-Treatment Histopathologic Images. 利用治疗前组织病理学图像预测三阴性乳腺癌的新辅助化疗反应。

ArXiv Pub Date : 2025-07-26

Hikmat Khan, Ziyu Su, Huina Zhang, Yihong Wang, Bohan Ning, Shi Wei, Hua Guo, Zaibo Li, Muhammad Khalid Khan Niazi

{"title":"Predicting Neoadjuvant Chemotherapy Response in Triple-Negative Breast Cancer Using Pre-Treatment Histopathologic Images.","authors":"Hikmat Khan, Ziyu Su, Huina Zhang, Yihong Wang, Bohan Ning, Shi Wei, Hua Guo, Zaibo Li, Muhammad Khalid Khan Niazi","doi":"","DOIUrl":"","url":null,"abstract":"Triple-negative breast cancer (TNBC) remains a major clinical challenge due to its aggressive behavior and lack of targeted therapies. Accurate early prediction of response to neoadjuvant chemotherapy (NACT) is essential for guiding personalized treatment strategies and improving patient outcomes. In this study, we present an attention-based multiple instance learning (MIL) framework designed to predict pathologic complete response (pCR) directly from pre-treatment hematoxylin and eosin (H&E)-stained biopsy slides. The model was trained on a retrospective in-house cohort of 174 TNBC patients and externally validated on an independent cohort (n = 30). It achieved a mean area under the curve (AUC) of 0.85 during five-fold cross-validation and 0.78 on external testing, demonstrating robust predictive performance and generalizability. To enhance model interpretability, attention maps were spatially co-registered with multiplex immuno-histochemistry (mIHC) data stained for PD-L1, CD8+ T cells, and CD163+ macrophages. The attention regions exhibited moderate spatial overlap with immune-enriched areas, with mean Intersection over Union (IoU) scores of 0.47 for PD-L1, 0.45 for CD8+ T cells, and 0.46 for CD163+ macrophages. The presence of these biomarkers in high-attention regions supports their biological relevance to NACT response in TNBC. This not only improves model interpretability but may also inform future efforts to identify clinically actionable histological biomarkers directly from H&E-stained biopsy slides, further supporting the utility of this approach for accurate NACT response prediction and advancing precision oncology in TNBC.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12310126/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144755373","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

Dual Mechanisms for Heterogeneous Responses of Inspiratory Neurons to Noradrenergic Modulation. 吸气神经元对去甲肾上腺素能调节的异质反应的双重机制。

ArXiv Pub Date : 2025-07-25

Sreshta Venkatakrishnan, Andrew K Tryba, Alfredo J Garcia Rd, Yangyang Wang

{"title":"Dual Mechanisms for Heterogeneous Responses of Inspiratory Neurons to Noradrenergic Modulation.","authors":"Sreshta Venkatakrishnan, Andrew K Tryba, Alfredo J Garcia Rd, Yangyang Wang","doi":"","DOIUrl":"","url":null,"abstract":"Respiration is an essential involuntary function necessary for survival. This poses a challenge for the control of breathing. The preB\"otzinger complex (preB\"otC) is a heterogeneous neuronal network responsible for driving the inspiratory rhythm. While neuromodulators such as norepinephrine (NE) allow it to be both robust and flexible for all living beings to interact with their environment, the basis for how neuromodulation impacts neuron-specific properties remains poorly understood. In this work, we examine how NE influences different preB\"otC neuronal subtypes by modeling its effects through modulating two key parameters: calcium-activated nonspecific cationic current gating conductance ($g_{rm CAN}$) and inositol-triphosphate ($rm IP_3$), guided by experimental studies. Our computational model captures the experimentally observed differential effects of NE on distinct preB\"otC bursting patterns. We show that this dual mechanism is critical for inducing conditional bursting and identify specific parameter regimes where silent neurons remain inactive in the presence of NE. Furthermore, using methods of dynamical systems theory, we uncover the mechanisms by which NE differentially modulates burst frequency and duration in NaP-dependent and CAN-dependent bursting neurons. These results align well with previously reported experimental findings and provide a deeper understanding of cell-specific neuromodulatory responses within the respiratory network.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12310139/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144755362","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

Multisession Longitudinal Dynamic MRI Incorporating Patient-Specific Prior Image Information Across Time. 结合患者特定先验图像信息的多时段纵向动态MRI。

ArXiv Pub Date : 2025-07-25

Jingjia Chen, Hersh Chandarana, Daniel K Sodickson, Li Feng

{"title":"Multisession Longitudinal Dynamic MRI Incorporating Patient-Specific Prior Image Information Across Time.","authors":"Jingjia Chen, Hersh Chandarana, Daniel K Sodickson, Li Feng","doi":"","DOIUrl":"","url":null,"abstract":"Serial Magnetic Resonance Imaging (MRI) exams are often performed in clinical practice, offering shared anatomical and motion information across imaging sessions. However, existing reconstruction methods process each session independently without leveraging this valuable longitudinal information. In this work, we propose a novel concept of longitudinal dynamic MRI, which incorporates patient-specific prior images to exploit temporal correlations across sessions. This framework enables progressive acceleration of data acquisition and reduction of scan time as more imaging sessions become available. The concept is demonstrated using the 4D Golden-angle RAdial Sparse Parallel (GRASP) MRI, a state-of-the-art dynamic imaging technique. Longitudinal reconstruction is performed by concatenating multi-session time-resolved 4D GRASP datasets into an extended dynamic series, followed by a low-rank subspace-based reconstruction algorithm. A series of experiments were conducted to evaluate the feasibility and performance of the proposed method. Results show that longitudinal 4D GRASP reconstruction consistently outperforms standard single-session reconstruction in image quality, while preserving inter-session variations. The approach demonstrated robustness to changes in anatomy, imaging intervals, and body contour, highlighting its potential for improving imaging efficiency and consistency in longitudinal MRI applications. More generally, this work suggests a new context-aware imaging paradigm in which the more we see a patient, the faster we can image.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12310133/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144755369","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

Optimal sensing through phase separation. 通过相位分离实现最佳传感。

ArXiv Pub Date : 2025-07-25

Henry Alston, Mason Rouches, Arvind Murugan, Aleksandra M Walczak, Thierry Mora

引用次数: 0

SAM2-Aug: Prior knowledge-based Augmentation for Target Volume Auto-Segmentation in Adaptive Radiation Therapy Using Segment Anything Model 2. 基于先验知识的自适应放射治疗靶体自动分割方法[j]。

ArXiv Pub Date : 2025-07-25

Guoping Xu, Yan Dai, Hengrui Zhao, Ying Zhang, Jie Deng, Weiguo Lu, You Zhang

{"title":"SAM2-Aug: Prior knowledge-based Augmentation for Target Volume Auto-Segmentation in Adaptive Radiation Therapy Using Segment Anything Model 2.","authors":"Guoping Xu, Yan Dai, Hengrui Zhao, Ying Zhang, Jie Deng, Weiguo Lu, You Zhang","doi":"","DOIUrl":"","url":null,"abstract":"Purpose: Accurate tumor segmentation is vital for adaptive radiation therapy (ART) but remains time-consuming and user-dependent. Segment Anything Model 2 (SAM2) shows promise for prompt-based segmentation but struggles with tumor accuracy. We propose prior knowledge-based augmentation strategies to enhance SAM2 for ART.Methods: Two strategies were introduced to improve SAM2: (1) using prior MR images and annotations as contextual inputs, and (2) improving prompt robustness via random bounding box expansion and mask erosion/dilation. The resulting model, SAM2-Aug, was fine-tuned and tested on the One-Seq-Liver dataset (115 MRIs from 31 liver cancer patients), and evaluated without retraining on Mix-Seq-Abdomen (88 MRIs, 28 patients) and Mix-Seq-Brain (86 MRIs, 37 patients).Results: SAM2-Aug outperformed convolutional, transformer-based, and prompt-driven models across all datasets, achieving Dice scores of 0.86(liver), 0.89(abdomen), and 0.90(brain). It demonstrated strong generalization across tumor types and imaging sequences, with improved performance in boundary-sensitive metrics.Conclusions: Incorporating prior images and enhancing prompt diversity significantly boosts segmentation accuracy and generalizability. SAM2-Aug offers a robust, efficient solution for tumor segmentation in ART. Code and models will be released at https://github.com/apple1986/SAM2-Aug.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12310124/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144755374","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

Competing chemical gradients change chemotactic dynamics and cell distribution. 竞争的化学梯度改变了趋化动力学和细胞分布。

ArXiv Pub Date : 2025-07-25

Emiliano Perez Ipiña, Brian A Camley

{"title":"Competing chemical gradients change chemotactic dynamics and cell distribution.","authors":"Emiliano Perez Ipiña, Brian A Camley","doi":"","DOIUrl":"","url":null,"abstract":"Cells are constantly exposed to diverse stimuli-chemical, mechanical, or electrical-that guide their movement. In physiological conditions, these signals often overlap, as seen during infections, where neutrophils and dendritic cells navigate through multiple chemotactic fields. How cells integrate and prioritize competing signals remains unclear. For instance, in the presence of opposing chemoattractant gradients, how do cells decide which direction to go? When should local signals dominate distant ones? A key factor in these processes is the precision with which cells sense each gradient, which depends non-monotonically on concentrations. Here, we study how gradient sensing accuracy shapes cell navigation in the presence of two distinct chemoattractant sources. We model cells as active random walkers that sense local gradients and combine these estimates to reorient their movement. Our results show that cells sensing multiple gradients can display a range of chemotactic behaviors, including anisotropic spatial patterns and varying degrees of confinement, depending on gradient shape and source location. The model also predicts cases where cells exhibit multistep navigation across sources or a hierarchical response toward one source, driven by disparities in their sensitivity to each chemoattractant. These findings highlight the role of gradient sensing in shaping spatial organization and navigation strategies in multi-field chemotaxis.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12310138/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144755358","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

Estimating Sensitivity Maps for X-Nuclei Magnetic Resonance Spectroscopic Imaging. 估计x核磁共振光谱成像的灵敏度图。

ArXiv Pub Date : 2025-07-24

Nicholas Dwork, Jeremy W Gordon, Shuyu Tang, Peder E Z Larson

引用次数: 0