The Computational Linguistics of Biological Sequences

Classics (Collection 2)

Shortly after Watson and Crick's discovery of the structure of DNA, and at about the same time that the genetic code and the essential facts of gene expression were being elucidated, the field of linguistics was being similarly revolutionized by the work of Noam Chomsky [Chomsky, 1955, 1957, 1959, 1963, 1965]. Observing that a seemingly infinite variety of language was available to individual human beings based on clearly finite resources and experience, he proposed a formal representation of the rules or syntax of language, called generative grammar, that could provide finite--indeed, concise--characterizations of such infinite languages. Just as the breakthroughs in molecular biology in that era served to anchor genetic concepts in physical structures and opened up entirely novel experimental paradigms, so did Chomsky's insight serve to energize the field of linguistics, with putative correlates of cognitive processes that could for the first time be reasoned about 48 ARTIFICIAL INTELLIGENCE & MOLECULAR BIOLOGY While Chomsky and his followers built extensively upon this foundation in the field of linguistics, generative grammars were also soon integrated into the framework of the theory of computation, and in addition now form the basis for efforts of computational linguists to automate the processing and understanding of human language. Since it is quite commonly asserted that DNA is a richly-expressive language for specifying the structures and processes of life, also with the potential for a seemingly infinite variety, it is surprising that relatively little has been done to apply to biological sequences the extensive results and methods developed over the intervening decades in the field of formal language theory. While such an approach has been proposed [Brendel and Busse, 1984], most investigations along these lines have used grammar formalisms as tools for what are essentially information-theoretic studies [Ebeling and Jimenez-Montano, 1980; Jimenez-Montano, 1984], or have involved statistical analyses at the level of vocabularies (reflecting a more traditional notion of comparative linguistics) [Brendel et al., 1986; Pevzner et al., 1989a,b; Pietrokovski et al., 1990].

Why a Diagram is (sometimes) Worth Ten Thousand Words


When two representations are informationally equivalent, their computational efficiency depends on the information-processing operators that act on them. Two sets of operators may differ in their capabilities for recognizing patterns, in the inferences they can carry out directly, and in their control strategies (in par- ticular, the control of search). Cognitive Science 11, 65-99

Diagnostic reasoning based on structure and behavior


See also:Retrospective on "Diagnostic reasoning based on structure and behavior", Artificial Intelligence (59) 1993 149-157MIT A.I. Memo 739, June, 1984Artificial Intelligence 24:347-410