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Inductive Inference over MacroMolecules. L.Allison, C.S.Wallace and C.N.Yee.
 

Summary: Inductive Inference over Macro­Molecules.
L.Allison, C.S.Wallace and C.N.Yee.
Department of Computer Science,
Monash University,
Australia 3168.
November 14, 1990
uucp: xxx@bruce.cs.monash.edu.au xxx=[lloyd, csw, cyee]
Supported by Australian Research Council grant A48830856
A preliminary version of this paper was presented at a joint session of the AAAI Spring Symposia on
Artificial Intelligence and Molecular Biology and on the Theory and Application of Minimal Length
Encoding, at Stanford, March 27­29 1990.
A fuller version titled `Finite­State Models in the Alignment of Macromolecules' later appeared in
J. Mol. Evol. 35 77-89 1992.
Abstract. Minimum message length techniques are applied to problems over strings such as biological
macro­molecules. This allows the posterior odds­ratio of two theories or hypotheses about strings to be
calculated. Given two strings we compare the r­theory, that they are related, with the null­theory, that they
are not related. This is done for one, three and five­state models of relation. Models themselves can be
compared and this is done on real DNA strings and artificial data. A fast, approximate MML string
comparison algorithm is also described.
Keywords. alignment, edit distance, homology, inductive inference, MML, similarity, string.

  

Source: Allison, Lloyd - Caulfield School of Information Technology, Monash University

 

Collections: Computer Technologies and Information Sciences