自我构建的机器：Lisp家族的长处 语言有助于构建复杂而灵活的生物信息模型

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摘要翻译：
我们解决了在生物信息学和计算生物学研究中扩展Lisp系列编程语言的需求。这个家族的语言，如Common Lisp、Scheme或Clojure，有助于创建强大而灵活的软件模型，这些模型是复杂而快速发展的领域（如生物学）所需要的。我们将指出将Lisp家族语言与其他编程语言区别开来的几个重要的关键特性，我们将解释这些特性如何帮助研究人员提高效率并创建更好的代码。我们还将展示这些特性如何使这些语言成为人工智能和机器学习应用程序的理想工具。我们将特别强调特定领域语言(DSL)的优势：专门用于特定领域的语言，因此不仅有助于更容易地制定研究问题，而且有助于建立适用于手头特定研究领域的标准和最佳编程实践。DSL特别容易用通用Lisp构建，通用Lisp是最全面的Lisp方言，通常被称为“可编程编程语言”。我们相信，Lisp赋予程序员前所未有的力量，可以构建日益复杂的人工智能系统，最终可能改变生物信息学和计算生物学中的机器学习和人工智能研究。
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英文标题：
《The Machine that Builds Itself: How the Strengths of Lisp Family
  Languages Facilitate Building Complex and Flexible Bioinformatic Models》
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作者：
Bohdan B. Khomtchouk, Edmund Weitz, Claes Wahlestedt
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最新提交年份：
2016
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分类信息：

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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一级分类：Computer Science        计算机科学
二级分类：Software Engineering        软件工程
分类描述：Covers design tools, software metrics, testing and debugging, programming environments, etc. Roughly includes material in all of ACM Subject Classes D.2, except that D.2.4 (program verification) should probably have Logics in Computer Science as the primary subject area.
涵盖设计工具、软件度量、测试和调试、编程环境等。大致包括ACM所有主题课程D.2的材料，除了D.2.4（程序验证）可能应该有计算机科学中的逻辑作为主要主题领域。
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英文摘要：
  We address the need for expanding the presence of the Lisp family of programming languages in bioinformatics and computational biology research. Languages of this family, like Common Lisp, Scheme, or Clojure, facilitate the creation of powerful and flexible software models that are required for complex and rapidly evolving domains like biology. We will point out several important key features that distinguish languages of the Lisp family from other programming languages and we will explain how these features can aid researchers in becoming more productive and creating better code. We will also show how these features make these languages ideal tools for artificial intelligence and machine learning applications. We will specifically stress the advantages of domain-specific languages (DSL): languages which are specialized to a particular area and thus not only facilitate easier research problem formulation, but also aid in the establishment of standards and best programming practices as applied to the specific research field at hand. DSLs are particularly easy to build in Common Lisp, the most comprehensive Lisp dialect, which is commonly referred to as the "programmable programming language." We are convinced that Lisp grants programmers unprecedented power to build increasingly sophisticated artificial intelligence systems that may ultimately transform machine learning and AI research in bioinformatics and computational biology.
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PDF链接：
https://arxiv.org/pdf/1608.02621

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关键词：生物信息 ISP Quantitative Intelligence Environments computational Common 有助于 标准 will