@inproceedings{4d272aef06384174b23180285eb65c36,
title = "Recognizing Splicing Junction Acceptors in Eukaryotic Genes Using Hidden Markov Models and Machine Learning Methods",
abstract = "The development of the hidden Markov model (HMM) acceptor model for splicing junction acceptor sites recognition was discussed. An HMM with 16 states and a set of transitions was defined for modeling a true acceptor site. The states and transitions were represented as a digraph where states corresponded to vertices and transitions to edges. Each state was associated with a discrete output probability distribution. The performance evaluation of the HMM system for true acceptor sites showed that on average, the system correctly detected 91.9% of the true acceptor sites in the test data.",
author = "Yin, {Michael M.} and Wang, {Jason T.L.}",
year = "2000",
language = "English (US)",
isbn = "0964345692",
series = "Proceedings of the Joint Conference on Information Sciences",
number = "2",
pages = "786--789",
editor = "P.P. Wang and P.P. Wang",
booktitle = "Proceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000, Volume 2",
edition = "2",
note = "Proceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000 ; Conference date: 27-02-2000 Through 03-03-2000",
}