文本文献的集合分类 T细胞交叉调节动力学中的自组织

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摘要翻译：
我们提出并研究了一个基于Agent的适应性免疫系统T细胞交叉调节模型，并将其应用于二元分类。我们的方法扩展了现有的T细胞交叉调节分析模型（Carneiro et al.in Immunol Rev 216(1):48-68，2007)，该模型用于研究单个T细胞群体与能够提呈单个抗原的理想化抗原提呈细胞相互作用的自组织动力学。通过基于Agent的建模，我们能够研究不同T细胞群体的自组织动力学，这些细胞通过呈现数百种不同抗原的抗原提呈细胞相互作用。此外，我们还表明，这种自组织动力学可以被引导产生有效的抗原二元分类，当应用于生物医学文本分类时，这种分类方法与现有的机器学习方法具有竞争力。更具体地说，我们在BioCreative challenge（Krallinger in the BioCreative II.5 challenge overview，p19，2009)提供的公开全文生物医学文章的数据集上测试我们的模型。我们研究了我们的模型参数配置的鲁棒性，并表明它导致了与最先进的分类器相媲美的令人鼓舞的结果。我们的结果帮助我们理解T细胞交叉调节作为引导自组织的一般原则，以及它在文档分类中的适用性。因此，我们的生物启发算法是一个有希望的生物医学文章分类和一般的二元文档分类的新方法。
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英文标题：
《Collective Classification of Textual Documents by Guided
  Self-Organization in T-Cell Cross-Regulation Dynamics》
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作者：
Alaa Abi-Haidar and Luis M. Rocha
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Information Retrieval        信息检索
分类描述：Covers indexing, dictionaries, retrieval, content and analysis. Roughly includes material in ACM Subject Classes H.3.0, H.3.1, H.3.2, H.3.3, and H.3.4.
涵盖索引，字典，检索，内容和分析。大致包括ACM主题课程H.3.0、H.3.1、H.3.2、H.3.3和H.3.4中的材料。
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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。
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Physics        物理学
二级分类：Adaptation and Self-Organizing Systems        自适应和自组织系统
分类描述：Adaptation, self-organizing systems, statistical physics, fluctuating systems, stochastic processes, interacting particle systems, machine learning
自适应，自组织系统，统计物理，波动系统，随机过程，相互作用粒子系统，机器学习
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一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
  We present and study an agent-based model of T-Cell cross-regulation in the adaptive immune system, which we apply to binary classification. Our method expands an existing analytical model of T-cell cross-regulation (Carneiro et al. in Immunol Rev 216(1):48-68, 2007) that was used to study the self-organizing dynamics of a single population of T-Cells in interaction with an idealized antigen presenting cell capable of presenting a single antigen. With agent-based modeling we are able to study the self-organizing dynamics of multiple populations of distinct T-cells which interact via antigen presenting cells that present hundreds of distinct antigens. Moreover, we show that such self-organizing dynamics can be guided to produce an effective binary classification of antigens, which is competitive with existing machine learning methods when applied to biomedical text classification. More specifically, here we test our model on a dataset of publicly available full-text biomedical articles provided by the BioCreative challenge (Krallinger in The biocreative ii. 5 challenge overview, p 19, 2009). We study the robustness of our model's parameter configurations, and show that it leads to encouraging results comparable to state-of-the-art classifiers. Our results help us understand both T-cell cross-regulation as a general principle of guided self-organization, as well as its applicability to document classification. Therefore, we show that our bio-inspired algorithm is a promising novel method for biomedical article classification and for binary document classification in general.
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PDF链接：
https://arxiv.org/pdf/1102.1027

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关键词：动力学 自组织 Quantitative Organization Intelligence cross organizing 文章 能够 模型