Using in silico models to predict lymphocyte activation and development in a data rich era

Salim I Khakoo , Jayajit Das
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引用次数: 0

Abstract

It has become a routine to get insights into the multi-scale nature of immune response in health and disease through ‘omics datasets. This presents us with a unique opportunity to leverage our access to such data to develop computational models that can generate usable predictions and mechanistic insights capable of seeding new ideas. However, this is a particularly challenging task due to the difficulty in integrating data and processes across multiple scales. In this review we discuss some of the challenges associated with this task and also the recent advances and opportunities that will help to makes these tractable, using the innate lymphocyte, the natural killer cell as an exemplar.

在数据丰富的时代,利用硅学模型预测淋巴细胞的活化和发展
通过'omics'数据集深入了解健康和疾病中免疫反应的多尺度性质已成为一种惯例。这为我们提供了一个难得的机会,利用我们对这些数据的访问来开发计算模型,从而产生可用的预测和机理见解,从而产生新的想法。然而,由于难以整合多个尺度的数据和过程,这是一项特别具有挑战性的任务。在这篇综述中,我们将以先天性淋巴细胞--自然杀伤细胞为例,讨论与这项任务相关的一些挑战,以及有助于应对这些挑战的最新进展和机遇。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Immunoinformatics (Amsterdam, Netherlands)
Immunoinformatics (Amsterdam, Netherlands) Immunology, Computer Science Applications
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