来自杂志 Nature protocols 的文献。
当前共找到 2 篇文献分享。
1.
周周复始 (2023-03-27 11:08):
#paper doi:https://doi.org/10.1038/s41596-023-00806-x. iBEAT V2.0: a multisite-applicable, deep learning-based pipeline for infant cerebral cortical surface reconstruction.2023.为了研究正常和异常的早期大脑发育,用不同的扫描仪和成像方案从多个站点收集了许多婴儿大脑磁共振成像(MRI)。但利用这些多站点成像数据精确地处理和量化婴儿的大脑发育是极具挑战性的,因为髓鞘持续形成和成熟而导致的极低和动态的组织对比,以及由于使用不同的成像协议/扫描仪而导致的不同站点间的数据异质性。现有的计算工具和pipeline通常在婴儿MRI数据上表现不佳。为了解决这些挑战,本文提出了一个鲁棒的、多站点适用的、婴儿定制的计算pipeline,它利用强大的深度学习技术。主要功能包括预处理、脑颅骨剥离、组织分割、拓扑校正、皮层表面表面重建和测量。可以很好地处理T1w和大范围(从出生到6岁)的婴儿大脑结构MRI,并且对不同成像协议/扫描仪差异是有效的,尽管只在BCP上训练。在多站点、多模态和多年龄数据集上与现有方法进行了广泛的比较证明ibeat具有优越的有效性、准确性和鲁棒性。
IF:13.100Q1 Nature protocols, 2023-05. DOI: 10.1038/s41596-023-00806-x PMID: 36869216
Abstract:
The human cerebral cortex undergoes dramatic and critical development during early postnatal stages. Benefiting from advances in neuroimaging, many infant brain magnetic resonance imaging (MRI) datasets have been collected from … >>>
The human cerebral cortex undergoes dramatic and critical development during early postnatal stages. Benefiting from advances in neuroimaging, many infant brain magnetic resonance imaging (MRI) datasets have been collected from multiple imaging sites with different scanners and imaging protocols for the investigation of normal and abnormal early brain development. However, it is extremely challenging to precisely process and quantify infant brain development with these multisite imaging data because infant brain MRI scans exhibit (a) extremely low and dynamic tissue contrast caused by ongoing myelination and maturation and (b) inter-site data heterogeneity resulting from the use of diverse imaging protocols/scanners. Consequently, existing computational tools and pipelines typically perform poorly on infant MRI data. To address these challenges, we propose a robust, multisite-applicable, infant-tailored computational pipeline that leverages powerful deep learning techniques. The main functionality of the proposed pipeline includes preprocessing, brain skull stripping, tissue segmentation, topology correction, cortical surface reconstruction and measurement. Our pipeline can handle both T1w and T2w structural infant brain MR images well in a wide age range (from birth to 6 years of age) and is effective for different imaging protocols/scanners, despite being trained only on the data from the Baby Connectome Project. Extensive comparisons with existing methods on multisite, multimodal and multi-age datasets demonstrate superior effectiveness, accuracy and robustness of our pipeline. We have maintained a website, iBEAT Cloud, for users to process their images with our pipeline ( http://www.ibeat.cloud ), which has successfully processed over 16,000 infant MRI scans from more than 100 institutions with various imaging protocols/scanners. <<<
翻译
2.
颜林林 (2022-08-08 07:54):
#paper doi:10.1038/s41596-022-00728-0 Nature Protocols, 2022, I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction. 目前,关于蛋白质结构预测的工具,大多都只能处理单结构域蛋白。然而,自然界中广泛存在的蛋白质,更多是具有多个结构域的,各结构域之间会协同发挥功能,因此亟需开发对这类蛋白质进行结构及功能预测的算法工具。本文提供了一个流程,名为I-TASSER-MTD,用于多结构域蛋白质的结构与功能预测。通过整合如下步骤:基于序列分析结构域(sequence-based domain parsing)、单结构域结构折叠(single-domain structure folding)、结构域之间的结构组装(inter-domain structure assembly)、基于结构的功能注释(structure-based function annotation),并且在各个步骤中都引入了深度学习,以及整合其他诸如蛋白质交联、冷冻电镜等实验数据,来提升相应的准确度,从而提高整体的蛋白质结构功能预测效果,并最终封装成为一套全自动的分析流程。
IF:13.100Q1 Nature protocols, 2022-10. DOI: 10.1038/s41596-022-00728-0 PMID: 35931779
Abstract:
Most proteins in cells are composed of multiple folding units (or domains) to perform complex functions in a cooperative manner. Relative to the rapid progress in single-domain structure prediction, there … >>>
Most proteins in cells are composed of multiple folding units (or domains) to perform complex functions in a cooperative manner. Relative to the rapid progress in single-domain structure prediction, there are few effective tools available for multi-domain protein structure assembly, mainly due to the complexity of modeling multi-domain proteins, which involves higher degrees of freedom in domain-orientation space and various levels of continuous and discontinuous domain assembly and linker refinement. To meet the challenge and the high demand of the community, we developed I-TASSER-MTD to model the structures and functions of multi-domain proteins through a progressive protocol that combines sequence-based domain parsing, single-domain structure folding, inter-domain structure assembly and structure-based function annotation in a fully automated pipeline. Advanced deep-learning models have been incorporated into each of the steps to enhance both the domain modeling and inter-domain assembly accuracy. The protocol allows for the incorporation of experimental cross-linking data and cryo-electron microscopy density maps to guide the multi-domain structure assembly simulations. I-TASSER-MTD is built on I-TASSER but substantially extends its ability and accuracy in modeling large multi-domain protein structures and provides meaningful functional insights for the targets at both the domain- and full-chain levels from the amino acid sequence alone. <<<
翻译
回到顶部