How the strengths of Lisp-family languages facilitate building complex and flexible bioinformatics applications

Abstract

We present a rationale for expanding the presence of the Lisp family of programming languages in bioinformatics and computational biology research. Put simply, Lisp-family languages enable programmers to more quickly write programs that run faster than in other languages. Languages such as Common Lisp, Scheme and Clojure facilitate the creation of powerful and flexible software that is 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 (DSLs): languages that 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 artificial intelligence research in bioinformatics and computational biology.

Pantry

“…The Lisp family of languages is especially suited to the creation of domain specific languages (DSL) due in part to its macro system. The ability to create new constructs allows for the creation of constructs especially suited for a particular domain, as can be seen in bioinformatic projects like Pathway Tools or BioBike [11] or big data [12].…”

Implementing cheminformatics

“…At a more fundamental level, there are different programming models, which require conceptually different approaches to designing an application. For example, Khomtchouk et al [14] suggest that the functional paradigm is best suited for scientific software development. On the other hand, Ray et al [15] show that projects using functional languages do not necessarily show better software quality.…”

The Architecture of Thought: Reflective Structures in Mental Constructs

“…For this paper, I add to this model by suggesting that our internal language of thought, and its results, are highly homoiconic. In programming, homoiconicity refers to the property of languages where code and data share the same structure, which means that the language can manipulate its own code as data, and vice versa [3]. Perhaps the most well known homoiconic language is Lisp, and indeed, Lisp was an early programming language that gained a lot of interest among those seeking to develop artificial general intelligence [4].…”