Constrained Markov networks for automated analysis of G-banded chromosomes.
Automated analysis of chromosome band patterns using probabilistic Markov networks has been reported in previous work. Band patterns are represented as strings of symbols. Inferred from a set of learning strings, a Markov network is a model of intraband and interband relations in these strings. The inference is entirely data-driven and is accomplished using dynamic programming. This paper presents a new model of chromosome band patterns, the constrained Markov network, which is a special case of its predecessor. Substantial experimental evidence of the superiority of the new model over the old is given in terms of equal results in centromere finding and improved results in classification for the 22 autosomes. Furthermore, a method for simplification of constrained Markov networks is shown to be of considerable importance with respect to computational complexity.
Department of Computer Science, University of Tennessee, Knoxville 37996-1301.
C
C
Guthrie
J
J
Gregor
M G
MG
Thomason
D002503:Centromere / Q000648:ultrastructure; D002871:Chromosome Banding; D002877:Chromosomes, Human / Q000648:ultrastructure*; D002965:Classification; D006801:Humans; D007621:Karyotyping; D016013:Likelihood Functions; D008390:Markov Chains; D008677:Metaphase; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted