Microbial gene identification using interpolated Markov models

Microbial gene identification using interpolated Markov models.

This paper describes a new system, GLIMMER, for finding genes in microbial genomes. In a series of tests on Haemophilus influenzae , Helicobacter pylori and other complete microbial genomes, this system has proven to be very accurate at locating virtually all the genes in these sequences, outperforming previous methods. A conservative estimate based on experiments on H.pylori and H. influenzae ...

Interpolated Markov models for eukaryotic gene finding.

Computational gene finding research has emphasized the development of gene finders for bacterial and human DNA. This has left genome projects for some small eukaryotes without a system that addresses their needs. This paper reports on a new system, GlimmerM, that was developed to find genes in the malaria parasite Plasmodium falciparum. Because the gene density in P. falciparum is relatively hi...

Interpolated Hidden Markov Models Estimated Using Conditional ML for Eukaryotic Gene Annotation

Participant Identification in Haptic Systems Using Hidden Markov Models

Biometric systems allow identification of individuals based on behavioral or physiological characteristics. In this paper we explore biometric applications in access control of haptic systems. These systems produce information on human computer interaction behavior of a specific participant and could potentially be unique. This paper proposes a novel design based on Hidden Markov Models (HMM). ...

Face Segmentation For Identification Using Hidden Markov Models

This paper details work done on face processing using a novel approach involving Hidden Markov Models. Experimental results from earlier work [14] indicated that left-to-right models with use of structural information yield better feature extraction than ergodic models. This paper illustrates how these hybrid models can be used to extract facial bands and automatically segment a face image into...