Abstract: HIV-1 genome is highly heterogeneous. Due to this
variation, features of HIV-I genome is in a wide range. For this
reason, the ability to infection of the virus changes depending on
different chemokine receptors. From this point of view, R5 HIV
viruses use CCR5 coreceptor while X4 viruses use CXCR5 and
R5X4 viruses can utilize both coreceptors. Recently, in
Bioinformatics, R5X4 viruses have been studied to classify by using
the experiments on HIV-1 genome.
In this study, R5X4 type of HIV viruses were classified using
Auto Regressive (AR) model through Artificial Neural Networks
(ANNs). The statistical data of R5X4, R5 and X4 viruses was
analyzed by using signal processing methods and ANNs. Accessible
residues of these virus sequences were obtained and modeled by AR
model since the dimension of residues is large and different from
each other. Finally the pre-processed data was used to evolve various
ANN structures for determining R5X4 viruses. Furthermore ROC
analysis was applied to ANNs to show their real performances. The
results indicate that R5X4 viruses successfully classified with high
sensitivity and specificity values training and testing ROC analysis
for RBF, which gives the best performance among ANN structures.
Abstract: Bioinformatics methods for predicting the T cell
coreceptor usage from the array of membrane protein of HIV-1 are
investigated. In this study, we aim to propose an effective prediction
method for dealing with the three-class classification problem of
CXCR4 (X4), CCR5 (R5) and CCR5/CXCR4 (R5X4). We made
efforts in investigating the coreceptor prediction problem as follows: 1)
proposing a feature set of informative physicochemical properties
which is cooperated with SVM to achieve high prediction test
accuracy of 81.48%, compared with the existing method with
accuracy of 70.00%; 2) establishing a large up-to-date data set by
increasing the size from 159 to 1225 sequences to verify the proposed
prediction method where the mean test accuracy is 88.59%, and 3)
analyzing the set of 14 informative physicochemical properties to
further understand the characteristics of HIV-1coreceptors.
Abstract: The feature of HIV genome is in a wide range because
of it is highly heterogeneous. Hence, the infection ability of the virus changes related with different chemokine receptors. From this point,
R5 and X4 HIV viruses use CCR5 and CXCR5 coreceptors respectively while R5X4 viruses can utilize both coreceptors. Recently, in Bioinformatics, R5X4 viruses have been studied to
classify by using the coreceptors of HIV genome.
The aim of this study is to develop the optimal Multilayer
Perceptron (MLP) for high classification accuracy of HIV sub-type viruses. To accomplish this purpose, the unit number in hidden layer
was incremented one by one, from one to a particular number. The statistical data of R5X4, R5 and X4 viruses was preprocessed by the
signal processing methods. Accessible residues of these virus sequences were extracted and modeled by Auto-Regressive Model
(AR) due to the dimension of residues is large and different from each other. Finally the pre-processed dataset was used to evolve MLP with various number of hidden units to determine R5X4
viruses. Furthermore, ROC analysis was used to figure out the optimal MLP structure.