A Generalized Hidden Markov Model for the Recognition of Human Genes in DNA
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
Two Methods for Improving Performance of a HMM and their Application for Gene Finding
Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology
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Hidden Markov models (HMMs) have proved to be a useful abstraction in modeling biological sequences. In some situations it is necessary to use generalized HMMs in order to model the length distributions of some sequence elements because basic HMMs force geometric-like distributions. In this paper we suggest the use of an arbitrary length distributions with geometric tails to model lengths of elements in biological sequences. We give an algorithm for annotation of a biological sequence in O(ndm2驴) time using such length distributions coupled with a suitable generalization of the HMM; here n is the length of the sequence, m is the number of states in the model, d is a parameter of the length distribution, and 驴 is a small constant dependent on model topology (compared to previously proposed algorithms with O(n3m2) time [10]). Our techniques can be incorporated into current software tools based on HMMs.To validate our approach, we demonstrate that many length distributions in gene finding can be accurately modeled with geometric-tail length distribution, keeping parameter d small.