PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A hidden Markov model for estimating retrovirus activities from expressed sequence databases
Merja Oja, Jaakko Peltonen and Samuel Kaski
In: 5th European Conference on Computational Biology (ECCB 2006), 21-24 Jan 2007, Eilat, Israel.

Abstract

Human endogenous retroviruses (HERVs) are remnants of ancient retrovirus infections and now reside within the human DNA. Recently HERV expression has been detected in both diseased patients and normal tissues. However, the expression levels of individual HERV sequences are mostly unknown. In this work we introduce a generative mixture model, based on Hidden Markov Models, for estimating the activities of the individual HERV sequences from databases of expressed sequences. We determined the relative expression levels of 91 HERVs; the majority of their activities were previously unknown.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:3527
Deposited By:Jaakko Peltonen
Deposited On:11 February 2008