dc.contributor.author | Halgaswaththa, Thilini | |
dc.contributor.author | Atukorale, Ajantha S. | |
dc.contributor.author | Jayawardena, Mahen | |
dc.contributor.author | Weerasena, Jagathpriya | |
dc.date.accessioned | 2012-09-05T14:40:38Z | |
dc.date.available | 2012-09-05T14:40:38Z | |
dc.date.issued | 2012-02-27 | |
dc.identifier.citation | p. 155-160 | en_US |
dc.identifier.isbn | 978-145771989-9 | |
dc.identifier.uri | http://ezproxy.unimap.edu.my:2080/stamp/stamp.jsp?tp=&arnumber=6178974 | |
dc.identifier.uri | http://dspace.unimap.edu.my/123456789/20845 | |
dc.description | Link to publisher's homepage at http://ieeexplore.ieee.org/ | en_US |
dc.description.abstract | Phylogeny is the primary tool used to understand the
evolutionary relationship between various taxonomic groups. If
someone finds an unknown bone fragment it is important to first
identify which “class of animal” that fragment may relate to. The
standard way would be to extract DNA, amplify and sequence a
conserved region and construct a phylogenetic tree and then
understand the Class to which it belongs. But this method
involves various tasks such as multiple sequences alignment and
constructing dendrogram by distance, parsimony or Bayesian
methods, which requires considerable time and effort. In this
research we implemented a probabilistic neural network to
understand the possible class of animal of an unknown DNA
sequence without using the phylogenetic tree approach. To
achieve this target we used 90 Transferring sequence and 400
sequences of NADH dehydrogenase subunit I coding region of
Mitochondrial DNA as data sets. The neural network was
trained using the inputs created based on codon count extracted
from the DNA sequences using tri gram method. The
performance of the neural network based analysis was compared
with phylogenetic analysis and the accuracy of the probabilistic
and feed forward neural network approaches were also
compared. Results revealed that the new approach performed
better than the standard phylogenetic approach. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.relation.ispartofseries | Proceedings of the International Conference on Biomedical Engineering (ICoBE 2012) | en_US |
dc.subject | Phylogenetics | en_US |
dc.subject | Probabilistic neural network | en_US |
dc.subject | Trigram method | en_US |
dc.subject | Feed forward neural networks | en_US |
dc.title | Neural network based phylogenetic analysis | en_US |
dc.type | Working Paper | en_US |
dc.contributor.url | mcj@ucsc.cmb.ac.lk | en_US |
dc.contributor.url | jagath@ibmbb.cmb.ac.lk | en_US |