Claes Wahlestedt

Bohdan B. Khomtchouk, Edmund Weitz, Claes Wahlestedt

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.

Bohdan B. Khomtchouk, Claes Wahlestedt

Zipf's law predicts a power-law relationship between word rank and frequency in language communication systems, and is widely reported in texts yet remains enigmatic as to its origins. Computer simulations have shown that language communication systems emerge at an abrupt phase transition in the fidelity of mappings between symbols and objects. Since the phase transition approximates the Heaviside or step function, we show that Zipfian scaling emerges asymptotically at high rank based on the Laplace transform. We thereby demonstrate that Zipf's law gradually emerges from the moment of phase transition in communicative systems. We show that this power-law scaling behavior explains the emergence of natural languages at phase transitions. We find that the emergence of Zipf's law during language communication suggests that the use of rare words in a lexicon is critical for the construction of an effective communicative system at the phase transition.