CpG islands play an important role in genes transcription regulation, due to the fact that these islands overlap with the genes’ promoter regions, and the methylation of those CpG islands may repress the transcription of the associated genes. Previou
s studies reported that
methylation of CpG islands is an important indicator of the presence and possibility of
developing cancers. There are mainly two types of algorithms to identify CpG islands in the nucleotides sequences: distance-based and sliding-window algorithms. The outputs of these algorithms are different for the same nucleotide sequence. The aim of this study is to compare the performance of the above mentioned algorithms by using two web tools named CpGCluster and newCpGReport.
CpG islands in human chromosome 22 were identified by applying the two algorithms on this chromosome, and the variation in the number and length of the identified islands was clear.
The results also show that about 60% of both tools’ output is crossed. Moreover, the effect of the traditional parameters of CpG islands (length, C+G content and Observed/expected ratio)
on the number of the identified islands was studied. The results show that the length parameter has a great effect on the number of islands identified by newCpGReport, while it does not affect CpGCluster’s performance. The effect of making CpG islands identified by
newCpGReport start and end with CpG was also studied, due to this operation C+G content and Observed/expected ratio increased for most islands, taking into account that 25% of the islands became shorter than 200 nucleotides.
The goal of this study is to model human body correctly, according to the principles and the standards
used to calculate the humanoid parameters. The model is built by using VN software and then it was
implemented in Matlab Simulink, in order to bu
ild a control system for simulating the humanoid balance
during standing. Precise and robust balance was reached by using PID controller with parameters
optimized by using genetic algorithm (GA). The control performance was tested by applying external
disturbance to the humanoid, the results show that the humanoid can retrieve its balance effectively.
It is found in this research to adopt a new classifier for diagnosing
Cardiac Arrhythmias depending on detecting the Electrocardiograph (ECG),
where the classifier can identify heart beats and extract its features. Using
these features we can deci
de if the heart beat is healthy or disordered.
Beside detection normal heart beats, the research focused on detection
two diseases:
1. Premature Ventricular Contraction PVC.
2. Premature Atrial Contraction PAC.
The new classifier diagnosed the two diseases with a very high quality
where the accuracy average is 97.56%.
The new classifier is developed depending on algorithms of ANFIS
Adaptive Neural Fuzzy Inference System. System includes two consecutive
neural networks; first one sorts the heart beats to two types: normal and
abnormal were the second diagnose the disease of the disordered heartbeats
only.
This new classifier offered higher levels of efficiency and accuracy in
the comparison with the internationally known classifiers.
