发帖
雷达卡
摘要翻译：
精确的免疫学模型提供了在硅胶中进行高通量实验的可能性，这种实验可以预测，或者至少暗示体内的现象。在本章中，我们比较了免疫记忆的各种模型。我们首先验证了作者开发的实验性免疫学模拟器，用已知的结果模拟了几种免疫学记忆理论。然后我们用同样的系统来评估免疫记忆理论的预测效果。由此产生的模型在人工免疫系统研究中还没有被探索过，我们将模拟的硅胶输出与体内测量进行了比较。尽管这一理论似乎是有效的，但我们认为有一套共同的理由为什么免疫记忆模型是一个有用的支持工具；本身不是决定性的。
---
英文标题：
《Modelling Immunological Memory》
---
作者：
Simon Garret, Martin Robbins, Joanne Walker, William Wilson, Uwe
Aickelin
---
最新提交年份：
2010
---
分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
--
一级分类：Computer Science 计算机科学
二级分类：Neural and Evolutionary Computing 神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
--
一级分类：Quantitative Biology 数量生物学
二级分类：Cell Behavior 细胞行为
分类描述：Cell-cell signaling and interaction; morphogenesis and development; apoptosis; bacterial conjugation; viral-host interaction; immunology
细胞-细胞信号传导及相互作用；形态发生和发育；细胞凋亡；细菌接合；病毒-宿主相互作用；免疫学
--
---
英文摘要：
Accurate immunological models offer the possibility of performing highthroughput experiments in silico that can predict, or at least suggest, in vivo phenomena. In this chapter, we compare various models of immunological memory. We first validate an experimental immunological simulator, developed by the authors, by simulating several theories of immunological memory with known results. We then use the same system to evaluate the predicted effects of a theory of immunological memory. The resulting model has not been explored before in artificial immune systems research, and we compare the simulated in silico output with in vivo measurements. Although the theory appears valid, we suggest that there are a common set of reasons why immunological memory models are a useful support tool; not conclusive in themselves.
---
PDF链接：
https://arxiv.org/pdf/1004.3932
京公网安备 11010802022788号
