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Patient-Specific CBCT Synthesis for Real-time Tumor Tracking in Surface-guided Radiotherapy. 用于体表引导放疗中实时肿瘤跟踪的患者特异性 CBCT 合成。
Pub Date : 2024-11-01
Shaoyan Pan, Vanessa Su, Junbo Peng, Junyuan Li, Yuan Gao, Chih-Wei Chang, Tonghe Wang, Zhen Tian, Xiaofeng Yang

In this work, we present a new imaging system to support real-time tumor tracking for surface-guided radiotherapy (SGRT). SGRT uses optical surface imaging (OSI) to acquire real-time surface topography images of the patient on the treatment couch. This serves as a surrogate for intra-fractional tumor motion tracking to guide radiation delivery. However, OSI cannot visualize internal anatomy, leading to motion tracking uncertainties for internal tumors, as body surface motion often does not have a good correlation with the internal tumor motion, particularly for lung cancer. This study proposes an Advanced Surface Imaging (A-SI) framework to address this issue. In the proposed A-SI framework, a high-speed surface imaging camera consistently captures surface images during radiation delivery, and a CBCT imager captures single-angle X-ray projections at low frequency. The A-SI then utilizes a generative model to generate real-time volumetric images with full anatomy, referred to as Optical Surface-Derived cone beam computed tomography (OSD-CBCT), based on the real-time high-frequent surface images and the low-frequency collected single-angle X-ray projections. The generated OSD-CBCT can provide accurate tumor motion for precise radiation delivery. The A-SI framework uses a patient-specific generative model: physics-integrated consistency-refinement denoising diffusion probabilistic model (PC-DDPM). This model leverages patient-specific anatomical structures and respiratory motion patterns derived from four-dimensional CT (4DCT) during treatment planning. It then employs a geometric transformation module (GTM) to extract volumetric anatomy information from the single-angle X-ray projection. A physics-integrated and cycle-consistency refinement strategy uses this information and the surface images to guide the DDPM, generating high quality OSD-CBCTs throughout the entire radiation delivery. A simulation study with 22 lung cancer patients evaluated the A-SI framework supported by PC-DDPM. The results showed that the framework produced real-time OSD-CBCT with high reconstruction fidelity and precise tumor localization. These results were validated through comprehensive intensity-, structural-, visual-, and clinical-level assessments. This study demonstrates the potential of A-SI to enable real-time tumor tracking with minimal imaging dose, advancing SGRT for motion-associated cancers and interventional procedures.

我们介绍了一种新的成像系统,用于支持表面引导放射治疗(SGRT)的实时肿瘤跟踪。SGRT 使用光学表面成像(OSI)来获取患者在治疗床上的实时表面形貌图像。然而,OSI 无法显示内部解剖结构。本研究提出了高级表面成像(A-SI)框架来解决这一问题。在所提出的 A-SI 框架中,高速表面成像摄像机会在放射治疗过程中持续捕捉表面图像,而 CBCT 成像仪则会以低频捕捉单角 X 射线投影。然后,A-SI 利用生成模型,根据实时高频表面图像和低频采集的单角 X 射线投影,生成具有完整解剖结构的实时容积图像,即光学表面衍生锥形束计算机断层扫描(OSD-CBCT)。生成的 OSD-CBCT 可以提供精确的肿瘤运动,从而实现精确放射。A-SI 框架使用患者特异性生成模型:物理集成一致性-改进去噪扩散概率模型(PC-DDPM)。该模型利用患者特定的解剖结构和四维 CT(4DCT)在治疗计划中得出的呼吸运动模式。然后,它采用几何变换模块(GTM)从单角 X 射线投影中提取容积解剖信息。一项针对 22 名肺癌患者的模拟研究对 PC-DDPM 支持的 A-SI 框架进行了评估。结果表明,该框架能生成具有高重建保真度和精确肿瘤定位的实时 OSD-CBCT。这项研究证明了 A-SI 在以最小的成像剂量实现实时肿瘤跟踪方面的潜力,从而推动了针对运动相关癌症和介入手术的 SGRT。
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引用次数: 0
Disentangling Interpretable Factors with Supervised Independent Subspace Principal Component Analysis. 用监督独立子空间主成分分析法分解可解释因素
Pub Date : 2024-10-31
Jiayu Su, David A Knowles, Raul Rabadan

The success of machine learning models relies heavily on effectively representing high-dimensional data. However, ensuring data representations capture human-understandable concepts remains difficult, often requiring the incorporation of prior knowledge and decomposition of data into multiple subspaces. Traditional linear methods fall short in modeling more than one space, while more expressive deep learning approaches lack interpretability. Here, we introduce Supervised Independent Subspace Principal Component Analysis ($texttt{sisPCA}$), a PCA extension designed for multi-subspace learning. Leveraging the Hilbert-Schmidt Independence Criterion (HSIC), $texttt{sisPCA}$ incorporates supervision and simultaneously ensures subspace disentanglement. We demonstrate $texttt{sisPCA}$'s connections with autoencoders and regularized linear regression and showcase its ability to identify and separate hidden data structures through extensive applications, including breast cancer diagnosis from image features, learning aging-associated DNA methylation changes, and single-cell analysis of malaria infection. Our results reveal distinct functional pathways associated with malaria colonization, underscoring the essentiality of explainable representation in high-dimensional data analysis.

机器学习模型的成功在很大程度上依赖于有效地表示高维数据。然而,确保数据表示捕捉人类可理解的概念仍然很困难,通常需要结合先验知识并将数据分解为多个子空间。传统的线性方法无法为多个空间建模,而更具表现力的深度学习方法则缺乏可解释性。在这里,我们引入了监督独立子空间主成分分析(Supervised Independent Subspace Principal Component Analysis,$texttt{sisPCA}$),这是一种专为多子空间学习而设计的 PCA 扩展。利用希尔伯特-施密特独立准则(Hilbert-Schmidt Independence Criterion,HSIC),$texttt{sisPCA}$结合了监督并同时确保子空间不纠缠。我们展示了$texttt{sisPCA}$与自动编码器和正则化线性回归的联系,并通过广泛的应用展示了其识别和分离隐藏数据结构的能力,包括从图像特征诊断乳腺癌、学习衰老相关的DNA甲基化变化以及疟疾感染的单细胞分析。我们的研究结果揭示了与疟疾定植相关的不同功能途径,强调了可解释表征在高维数据分析中的重要性。
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引用次数: 0
Unconditional stability of a recurrent neural circuit implementing divisive normalization. 实施除法归一化的递归神经回路的无条件稳定性
Pub Date : 2024-10-31
Shivang Rawat, David J Heeger, Stefano Martiniani

Stability in recurrent neural models poses a significant challenge, particularly in developing biologically plausible neurodynamical models that can be seamlessly trained. Traditional cortical circuit models are notoriously difficult to train due to expansive nonlinearities in the dynamical system, leading to an optimization problem with nonlinear stability constraints that are difficult to impose. Conversely, recurrent neural networks (RNNs) excel in tasks involving sequential data but lack biological plausibility and interpretability. In this work, we address these challenges by linking dynamic divisive normalization (DN) to the stability of "oscillatory recurrent gated neural integrator circuits" (ORGaNICs), a biologically plausible recurrent cortical circuit model that dynamically achieves DN and that has been shown to simulate a wide range of neurophysiological phenomena. By using the indirect method of Lyapunov, we prove the remarkable property of unconditional local stability for an arbitrary-dimensional ORGaNICs circuit when the recurrent weight matrix is the identity. We thus connect ORGaNICs to a system of coupled damped harmonic oscillators, which enables us to derive the circuit's energy function, providing a normative principle of what the circuit, and individual neurons, aim to accomplish. Further, for a generic recurrent weight matrix, we prove the stability of the 2D model and demonstrate empirically that stability holds in higher dimensions. Finally, we show that ORGaNICs can be trained by backpropagation through time without gradient clipping/scaling, thanks to its intrinsic stability property and adaptive time constants, which address the problems of exploding, vanishing, and oscillating gradients. By evaluating the model's performance on RNN benchmarks, we find that ORGaNICs outperform alternative neurodynamical models on static image classification tasks and perform comparably to LSTMs on sequential tasks.

循环神经模型的稳定性是一项重大挑战,尤其是在开发可无缝训练的生物学上可信的神经动力学模型方面。传统的大脑皮层电路模型由于动态系统中的扩展非线性而难以训练,导致优化问题中的非线性稳定性约束难以施加。相反,递归神经网络(RNN)在涉及序列数据的任务中表现出色,但缺乏生物合理性和可解释性。在这项工作中,我们通过将动态分裂归一化(DN)与 ORGaNICs 的稳定性联系起来来应对这些挑战。ORGaNICs 是一种生物学上可信的递归皮层电路模型,可动态实现 DN,并已被证明能模拟各种神经生理现象。通过使用李亚普诺夫的间接方法,我们证明了任意维度的 ORGaNICs 电路在递归权重矩阵为同一值时无条件局部稳定的显著特性。因此,我们将 ORGaNICs 与耦合阻尼谐振子系统联系起来,从而推导出电路的能量函数,为电路和单个神经元的目标提供了规范原理。此外,对于一般的递归权重矩阵,我们证明了二维模型的稳定性,并通过经验证明稳定性在更高维度上也是成立的。最后,我们证明 ORGaNICs 可以通过时间反向传播进行训练,而无需梯度剪切/缩放,这要归功于其固有的稳定性和自适应时间常数,它们解决了梯度爆炸、消失和振荡的问题。通过在 RNN 基准上评估该模型的性能,我们发现 ORGaNIC 在静态图像分类任务中的表现优于其他神经动力学模型,而在顺序任务中的表现则与 LSTM 不相上下。
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引用次数: 0
BAMITA: Bayesian Multiple Imputation for Tensor Arrays. BAMITA:张量阵列的贝叶斯多重估算。
Pub Date : 2024-10-30
Ziren Jiang, Gen Li, Eric F Lock

Data increasingly take the form of a multi-way array, or tensor, in several biomedical domains. Such tensors are often incompletely observed. For example, we are motivated by longitudinal microbiome studies in which several timepoints are missing for several subjects. There is a growing literature on missing data imputation for tensors. However, existing methods give a point estimate for missing values without capturing uncertainty. We propose a multiple imputation approach for tensors in a flexible Bayesian framework, that yields realistic simulated values for missing entries and can propagate uncertainty through subsequent analyses. Our model uses efficient and widely applicable conjugate priors for a CANDECOMP/PARAFAC (CP) factorization, with a separable residual covariance structure. This approach is shown to perform well with respect to both imputation accuracy and uncertainty calibration, for scenarios in which either single entries or entire fibers of the tensor are missing. For two microbiome applications, it is shown to accurately capture uncertainty in the full microbiome profile at missing timepoints and used to infer trends in species diversity for the population. Documented R code to perform our multiple imputation approach is available at https://github.com/lockEF/MultiwayImputation.

在一些生物医学领域,数据越来越多地采用多向阵列或张量的形式。这种张量通常观察不完全。例如,在微生物组纵向研究中,有几个研究对象的几个时间点缺失。关于张量缺失数据估算的文献越来越多。然而,现有方法只给出了缺失值的点估计,却没有捕捉到不确定性。我们在一个灵活的贝叶斯框架中提出了一种张量多重估算方法,它能为缺失条目提供真实的模拟值,并能在后续分析中传播不确定性。我们的模型采用了高效且广泛适用的共轭先验,用于 CANDECOMP/PARAFAC (CP) 因子分解,具有可分离的残差协方差结构。对于张量的单个条目或整个纤维缺失的情况,这种方法在估算精度和不确定性校准方面都表现良好。在两个微生物组应用中,该方法被证明能准确捕捉缺失时间点上完整微生物组剖面的不确定性,并用于推断种群的物种多样性趋势。执行多重估算方法的 R 代码文档见 https://github.com/lockEF/MultiwayImputation 。
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引用次数: 0
A stochastic explanation for observed local-to-global foraging states in Caenorhabditis elegans. 秀丽隐杆线虫局部到全球觅食状态的随机解释。
Pub Date : 2024-10-30
Andrew Margolis, Andrew Gordus

Abrupt changes in behavior can often be associated with changes in underlying behavioral states. When placed off food, the foraging behavior of C. elegans can be described as a change between an initial local-search behavior characterized by a high rate of reorientations, followed by a global-search behavior characterized by sparse reorientations. This is commonly observed in individual worms, but when numerous worms are characterized, only about half appear to exhibit this behavior. We propose an alternative model that predicts both abrupt and continuous changes to reorientation that does not rely on behavioral states. This model is inspired by molecular dynamics modeling that defines the foraging reorientation rate as a decaying parameter. By stochastically sampling from the probability distribution defined by this rate, both abrupt and gradual changes to reorientation rates can occur, matching experimentally observed results. Crucially, this model does not depend on behavioral states or information accumulation. Even though abrupt behavioral changes do occur, they are not necessarily indicative of abrupt changes in behavioral states, especially when abrupt changes are not universally observed in the population.

行为的突然变化通常与潜在行为状态的变化有关。当远离食物时,秀丽隐杆线虫的觅食行为可以描述为以高重定向率为特征的初始局部搜索行为和以稀疏重定向为特征的全局搜索行为之间的变化。这通常在单个蠕虫身上观察到,但当对大量蠕虫进行表征时,只有大约一半的蠕虫表现出这种行为。我们提出了一个替代模型,该模型预测不依赖于行为状态的突然和连续的重新定向变化。该模型的灵感来自分子动力学建模,该建模将觅食重新定向速率定义为衰减参数。通过从该速率定义的概率分布中随机采样,可以发生重新定向速率的突变和渐变,与实验观察到的结果相匹配。至关重要的是,这个模型不依赖于行为状态或信息积累。即使确实发生了突然的行为变化,它们也不一定预示着行为状态的突然变化,尤其是在人群中没有普遍观察到突然变化的情况下。
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引用次数: 0
Efficient high-resolution refinement in cryo-EM with stochastic gradient descent. 利用随机梯度下降技术在低温电子显微镜中进行高效高分辨率细化。
Pub Date : 2024-10-30
Bogdan Toader, Marcus A Brubaker, Roy R Lederman

Electron cryomicroscopy (cryo-EM) is an imaging technique widely used in structural biology to determine the three-dimensional structure of biological molecules from noisy two-dimensional projections with unknown orientations. As the typical pipeline involves processing large amounts of data, efficient algorithms are crucial for fast and reliable results. The stochastic gradient descent (SGD) algorithm has been used to improve the speed of ab initio reconstruction, which results in a first, low-resolution estimation of the volume representing the molecule of interest, but has yet to be applied successfully in the high-resolution regime, where expectation-maximization algorithms achieve state-of-the-art results, at a high computational cost. In this article, we investigate the conditioning of the optimization problem and show that the large condition number prevents the successful application of gradient descent-based methods at high resolution. Our results include a theoretical analysis of the condition number of the optimization problem in a simplified setting where the individual projection directions are known, an algorithm based on computing a diagonal preconditioner using Hutchinson's diagonal estimator, and numerical experiments showing the improvement in the convergence speed when using the estimated preconditioner with SGD. The preconditioned SGD approach can potentially enable a simple and unified approach to ab initio reconstruction and high-resolution refinement with faster convergence speed and higher flexibility, and our results are a promising step in this direction.

电子冷冻显微镜(cryo-EM)是一种广泛应用于结构生物学的成像技术,可从方向未知的嘈杂二维投影中确定生物分子的三维结构。由于典型的流水线需要处理大量数据,因此高效的算法对于获得快速可靠的结果至关重要。随机梯度下降(SGD)算法已被用于提高原子序数重建的速度,其结果是对代表感兴趣分子的体积进行首次低分辨率估计,但尚未成功应用于高分辨率机制,在该机制中,期望最大化算法以较高的计算成本实现了最先进的结果。在本文中,我们对优化问题的条件进行了研究,结果表明较大的条件数阻碍了基于梯度下降的方法在高分辨率下的成功应用。我们的研究结果包括:在已知各个投影方向的简化环境中,对优化问题条件数的理论分析;基于使用 Hutchinson 对角线估计器计算对角线预处理的算法;以及数值实验,实验结果表明在使用 SGD 的预处理估计器时,收敛速度有所提高。带预处理的 SGD 方法有可能以更快的收敛速度和更高的灵活性,为原子序数重建和高分辨率细化提供一种简单而统一的方法。
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引用次数: 0
MassSpecGym: A benchmark for the discovery and identification of molecules. MassSpecGym:发现和识别分子的基准。
Pub Date : 2024-10-30
Roman Bushuiev, Anton Bushuiev, Niek F de Jonge, Adamo Young, Fleming Kretschmer, Raman Samusevich, Janne Heirman, Fei Wang, Luke Zhang, Kai Dührkop, Marcus Ludwig, Nils A Haupt, Apurva Kalia, Corinna Brungs, Robin Schmid, Russell Greiner, Bo Wang, David S Wishart, Li-Ping Liu, Juho Rousu, Wout Bittremieux, Hannes Rost, Tytus D Mak, Soha Hassoun, Florian Huber, Justin J J van der Hooft, Michael A Stravs, Sebastian Böcker, Josef Sivic, Tomáš Pluskal

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym -- the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: textit{de novo} molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at url{https://github.com/pluskal-lab/MassSpecGym}.

发现和鉴定生物与环境样本中的分子对于推动生物医学和化学科学的发展至关重要。串联质谱(MS/MS)是高通量阐明分子结构的领先技术。然而,从质谱中解码分子结构是一项极具挑战性的工作,即使由人类专家来完成也是如此。因此,绝大多数获得的 MS/MS 图谱仍然无法解读,从而限制了我们对潜在(生物)化学过程的了解。尽管从 MS/MS 图谱预测分子结构的机器学习应用取得了几十年的进展,但由于缺乏标准数据集和评估协议,新方法的开发受到严重阻碍。为了解决这个问题,我们提出了 MassSpecGym -- 第一个从 MS/MS 数据中发现和识别分子的综合基准。我们的基准包括最大的公开高质量标记 MS/MS 图谱集,并定义了三个 MS/MS 注释挑战:文本{de novo}分子结构生成、分子检索和光谱模拟。它包括新的评估指标和泛化需求的数据拆分,从而实现了 MS/MS 注释任务的标准化,并使广泛的机器学习社区能够解决这一问题。MassSpecGym 在 url{https://github.com/pluskal-lab/MassSpecGym} 上公开发布。
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引用次数: 0
GENERATIVE FORECASTING OF BRAIN ACTIVITY ENHANCES ALZHEIMER'S CLASSIFICATION AND INTERPRETATION. 大脑活动的生成预测增强了阿尔茨海默氏症的分类和解释能力。
Pub Date : 2024-10-30
Yutong Gao, Vince D Calhoun, Robyn L Miller

Understanding the relationship between cognition and intrinsic brain activity through purely data-driven approaches remains a significant challenge in neuroscience. Resting-state functional magnetic resonance imaging (rs-fMRI) offers a non-invasive method to monitor regional neural activity, providing a rich and complex spatiotemporal data structure. Deep learning has shown promise in capturing these intricate representations. However, the limited availability of large datasets, especially for disease-specific groups such as Alzheimer's Disease (AD), constrains the generalizability of deep learning models. In this study, we focus on multivariate time series forecasting of independent component networks derived from rs-fMRI as a form of data augmentation, using both a conventional LSTM-based model and the novel Transformer-based BrainLM model. We assess their utility in AD classification, demonstrating how generative forecasting enhances classification performance. Post-hoc interpretation of BrainLM reveals class-specific brain network sensitivities associated with AD.

通过纯粹的数据驱动方法来理解认知与大脑内在活动之间的关系,仍然是神经科学领域的一项重大挑战。静息态功能磁共振成像(rs-fMRI)提供了一种监测区域神经活动的无创方法,提供了丰富而复杂的时空数据结构。深度学习已显示出捕捉这些复杂表征的前景。然而,大型数据集的可用性有限,尤其是针对阿尔茨海默病(AD)等特定疾病群体的数据集,限制了深度学习模型的普适性。在本研究中,我们使用基于 LSTM 的传统模型和基于 Transformer 的新型 BrainLM 模型,将重点放在对源自 rs-fMRI 的独立分量网络的多变量时间序列预测上,以此作为一种数据增强形式。我们评估了它们在 AD 分类中的实用性,展示了生成预测是如何提高分类性能的。对BrainLM的事后解释揭示了与AD相关的特定类别脑网络敏感性。
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引用次数: 0
Nanoscale Connectomics Annotation Standards Framework. 纳米尺度连接组学注释标准框架。
Pub Date : 2024-10-30
Nicole K Guittari, Miguel E Wimbish, Patricia K Rivlin, Mark A Hinton, Jordan K Matelsky, Victoria A Rose, Jorge L Rivera, Nicole E Stock, Brock A Wester, Erik C Johnson, William R Gray-Roncal

The promise of large-scale, high-resolution datasets from Electron Microscopy (EM) and X-ray Microtomography (XRM) lies in their ability to reveal neural structures and synaptic connectivity, which is critical for understanding the brain. Effectively managing these complex and rapidly increasing datasets will enable new scientific insights, facilitate querying, and support secondary use across the neuroscience community. However, without effective neurodata standards that permit use of these data across multiple systems and workflows, these valuable and costly datasets risk being underutilized especially as they surpass petascale levels. These standards will promote data sharing through accessible interfaces, allow researchers to build on each other's work, and guide the development of tools and capabilities that are interoperable. Herein we outline a standards framework for creating and managing annotations originating and derived from high-resolution volumetric imaging and connectomic datasets, focusing on ensuring Findable, Accessible, Interoperable, and Reusable (FAIR) practices. The goal is to enhance collaborative efforts, boost the reliability of findings, and enable comparative analysis across growing datasets of different species and modalities. We have formed a global working group with academic and industry partners in the high-resolution volumetric data generation and analysis community, focused on identifying gaps in current EM and XRM data pipelines, and refining outlines and platforms for standardizing EM and XRM methods. This focus considers existing and past community approaches and includes examining neuronal entities, biological components, and associated metadata, while emphasizing adaptability and fostering collaboration.

来自电子显微镜(EM)和 X 射线显微层析成像(XRM)的大规模、高分辨率数据集的前景在于其揭示神经结构和突触连接的能力,这对于理解大脑至关重要。有效管理这些复杂且快速增长的数据集将有助于获得新的科学见解、方便查询并支持神经科学界的二次利用。然而,如果没有有效的神经数据标准,允许在多个系统和工作流程中使用这些数据,这些宝贵而昂贵的数据集就有可能得不到充分利用,尤其是当它们超过千万亿次级别时。这些标准将通过可访问的接口促进数据共享,使研究人员能够在彼此工作的基础上开展研究,并指导开发具有互操作性的工具和功能。在此,我们概述了一个标准框架,用于创建和管理源自高分辨率容积成像和连接组学数据集的注释,重点是确保可查找、可访问、可互操作和可重用(FAIR)实践。我们的目标是加强合作,提高研究结果的可靠性,并在不同物种和模式的不断增长的数据集中进行比较分析。我们与高分辨率容积数据生成和分析领域的学术界和产业界合作伙伴成立了一个全球工作组,重点是找出当前 EM 和 XRM 数据管道中的差距,并完善 EM 和 XRM 方法标准化的大纲和平台。这一重点考虑了现有和过去的社区方法,包括检查神经元实体、生物组件和相关元数据,同时强调适应性和促进合作。
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引用次数: 0
Genetic studies through the lens of gene networks. 通过基因网络透视遗传学研究。
Pub Date : 2024-10-30
Marc Subirana-Granés, Jill Hoffman, Haoyu Zhang, Christina Akirtava, Sutanu Nandi, Kevin Fotso, Milton Pividori

Understanding the genetic basis of complex traits is a longstanding challenge in the field of genomics. Genome-wide association studies (GWAS) have identified thousands of variant-trait associations, but most of these variants are located in non-coding regions, making the link to biological function elusive. While traditional approaches, such as transcriptome-wide association studies (TWAS), have advanced our understanding by linking genetic variants to gene expression, they often overlook gene-gene interactions. Here, we review current approaches to integrate different molecular data, leveraging machine learning methods to identify gene modules based on co-expression and functional relationships. These integrative approaches, like PhenoPLIER, combine TWAS and drug-induced transcriptional profiles to effectively capture biologically meaningful gene networks. This integration provides a context-specific understanding of disease processes while highlighting both core and peripheral genes. These insights pave the way for novel therapeutic targets and enhance the interpretability of genetic studies in personalized medicine.

了解复杂性状的遗传基础是基因组学领域的一项长期挑战。全基因组关联研究(GWAS)发现了成千上万个变异与性状的关联,但这些变异大多位于非编码区,因此与生物功能的联系难以捉摸。虽然转录组关联研究(TWAS)等传统方法通过将遗传变异与基因表达联系起来加深了我们的理解,但它们往往忽略了基因与基因之间的相互作用。在此,我们回顾了目前整合不同分子数据的方法,利用机器学习方法根据共表达和功能关系识别基因模块。这些整合方法(如 PhenoPLIER)结合了 TWAS 和药物诱导转录图谱,可有效捕捉具有生物学意义的基因网络。这种整合提供了对疾病过程的特定背景理解,同时突出了核心和外围基因。这些见解为新的治疗目标铺平了道路,并提高了个性化医疗中基因研究的可解释性。
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引用次数: 0
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