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首页> 外文会议>International conference on computer aided systems theory >Classification of the States of Human Adaptive Immune Systems by Analyzing Immunoglobulin and T Cell Receptors Using ImmunExplorer
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Classification of the States of Human Adaptive Immune Systems by Analyzing Immunoglobulin and T Cell Receptors Using ImmunExplorer

机译：通过使用ImmunExplorer分析免疫球蛋白和T细胞受体，对人类适应性免疫系统的状态进行分类

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摘要

The behavior and actions of the human adaptive immune system and its key players, namely B and T cells, are often hard to understand in their entirety. We here present a workflow for modelling the states of adaptive immune systems by analyzing B and T cell receptor repertoires using next-generation sequencing data. For our workflow, we have blood and kidney tissues from diseased patients, who suffered from different kidney diseases (e.g., renal carcinoma), and healthy proband. A set of features based on clonal expansion and diversity of immunoglob-ulins and T cell receptor next-generation sequencing data, isolated from patients, are calculated. Using different machine learning methods such as support vector machines, random forests, artificial neural networks and genetic programming in HeuristicLab, we are able to classify and distinguish between healthy and diseased individuals up to 80% accuracy using ImmunExplorer.
机译：人类适应性免疫系统及其关键参与者（即B细胞和T细胞）的行为和行动通常很难完全理解。我们在这里提出了一种工作流程，用于通过使用下一代测序数据分析B和T细胞受体库来对适应性免疫系统的状态进行建模。对于我们的工作流程，我们有来自患病患者的血液和肾脏组织，这些患者患有不同的肾脏疾病（例如，肾癌）和健康的先证者。计算了基于克隆扩展和免疫球蛋白多样性以及从患者中分离出来的T细胞受体下一代测序数据的一系列特征。通过在HeuristicLab中使用不同的机器学习方法（例如支持向量机，随机森林，人工神经网络和基因编程），我们可以使用ImmunExplorer对健康和患病个体进行分类和区分，准确性最高可达80％。

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