Suppr超能文献

一个用于提高生物信息学和科学界对函数式编程可及性的开源沙盒。

An Open-Source Sandbox for Increasing the Accessibility of Functional Programming to the Bioinformatics and Scientific Communities.

作者信息

Fenwick Matthew, Sesanker Colbert, Schiller Martin R, Ellis Heidi Jc, Hinman M Lee, Vyas Jay, Gryk Michael R

机构信息

Department of Microbial, Molecular and Structural Biology, University of Connecticut Health Center, 263 Farmington Avenue Farmington, Connecticut 06030.

School of Life Sciences, University of Nevada Las Vegas, 4505 Maryland Pkwy., Las Vegas 89154-4004.

出版信息

Proc Int Conf Inf Technol New Gener. 2012;2012:89-94. doi: 10.1109/ITNG.2012.21.

Abstract

Scientists are continually faced with the need to express complex mathematical notions in code. The renaissance of functional languages such as LISP and Haskell is often credited to their ability to implement complex data operations and mathematical constructs in an expressive and natural idiom. The slow adoption of functional computing in the scientific community does not, however, reflect the congeniality of these fields. Unfortunately, the learning curve for adoption of functional programming techniques is steeper than that for more traditional languages in the scientific community, such as Python and Java, and this is partially due to the relative sparseness of available learning resources. To fill this gap, we demonstrate and provide applied, scientifically substantial examples of functional programming, We present a multi-language source-code repository for software integration and algorithm development, which generally focuses on the fields of machine learning, data processing, bioinformatics. We encourage scientists who are interested in learning the basics of functional programming to adopt, reuse, and learn from these examples. The source code is available at: https://github.com/CONNJUR/CONNJUR-Sandbox (see also http://www.connjur.org).

摘要

科学家们不断面临着用代码来表达复杂数学概念的需求。诸如LISP和Haskell等函数式语言的复兴,常常归功于它们能够以一种富有表现力且自然的方式来实现复杂的数据操作和数学结构。然而,函数式计算在科学界的缓慢采用,并不反映这些领域之间的契合度。不幸的是,在科学界,采用函数式编程技术的学习曲线比采用诸如Python和Java等更传统语言的学习曲线要陡峭,部分原因是可用学习资源相对稀少。为了填补这一空白,我们展示并提供了函数式编程的应用且具有科学实质内容的示例。我们提供了一个用于软件集成和算法开发的多语言源代码库,其主要聚焦于机器学习、数据处理、生物信息学等领域。我们鼓励对学习函数式编程基础感兴趣的科学家采用、复用并借鉴这些示例。源代码可在以下网址获取:https://github.com/CONNJUR/CONNJUR-Sandbox(另见http://www.connjur.org)。

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