Classification of CpG Islands in the Human Genome Based on the Interval Distance Distribution of Adjacent CG Sites

2009 ◽  
Author(s):  
Changle Qi ◽  
Xiaoming Wu ◽  
Lili Liu ◽  
Jianqiang Du ◽  
Bo Wang
Keyword(s):  
Human Genome ◽  
Cpg Islands ◽  
Distance Distribution ◽  
Interval Distance

Data Mining Technique Applied To DNA Sequencing

2015 ◽  
Vol 2 (7) ◽  
pp. 18
Author(s):  
R. Jamuna
Keyword(s):  
Data Mining ◽  
Dna Methylation ◽  
Markov Chain ◽  
Human Genome ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Effective Means ◽  
Cpg Islands ◽  
Chain Model ◽  
The Given

CpG islands (CGIs) play a vital role in genome analysis as genomic markers.  Identification of the CpG pair has contributed not only to the prediction of promoters but also to the understanding of the epigenetic causes of cancer. In the human genome [1] wherever the dinucleotides CG occurs the C nucleotide (cytosine) undergoes chemical modifications. There is a relatively high probability of this modification that mutates C into a T. For biologically important reasons the mutation modification process is suppressed in short stretches of the genome, such as ‘start’ regions. In these regions [2] predominant CpG dinucleotides are found than elsewhere. Such regions are called CpG islands. DNA methylation is an effective means by which gene expression is silenced. In normal cells, DNA methylation functions to prevent the expression of imprinted and inactive X chromosome genes. In cancerous cells, DNA methylation inactivates tumor-suppressor genes, as well as DNA repair genes, can disrupt cell-cycle regulation. The most current methods for identifying CGIs suffered from various limitations and involved a lot of human interventions. This paper gives an easy searching technique with data mining of Markov Chain in genes. Markov chain model has been applied to study the probability of occurrence of C-G pair in the given   gene sequence. Maximum Likelihood estimators for the transition probabilities for each model and analgously for the  model has been developed and log odds ratio that is calculated estimates the presence or absence of CpG is lands in the given gene which brings in many  facts for the cancer detection in human genome.

scholarly journals Particle Swarm Optimization with Reinforcement Learning for the Prediction of CpG Islands in the Human Genome

PLoS ONE ◽  
2011 ◽  
Vol 6 (6) ◽  
pp. e21036 ◽  
Author(s):  
Li-Yeh Chuang ◽  
Hsiu-Chen Huang ◽  
Ming-Cheng Lin ◽  
Cheng-Hong Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Reinforcement Learning ◽  
Human Genome ◽  
Cpg Islands ◽  
Particle Swarm ◽  
Swarm Optimization

scholarly journals Systematic palaeontology and biostratigraphy: are they “dated”?

1992 ◽  
Vol 4 (3) ◽  
pp. 257-257
Author(s):  
Art Boucot
Keyword(s):  
Human Genome ◽  
Systematic Description ◽  
Diminishing Returns ◽  
Systematic Palaeontology

There is probably something buried deeply within the human genome that urges man to collect, sort, and classify. How else is it possible to explain a child's collection of matchbook covers or Mendeleev's periodic classification of the elements? Systematic palaeontology and biostratigraphy turn this natural urge to very practical purposes, for the geologist and the evolutionist are dependent on the results. However, anyone reading the pages of certain palaeobiological journals, or a certain type of article on extinction, might be forgiven for concluding that we now have reached the point where further systematic description of fossils and further refining of our biostratigraphic classification had reached the point of diminishing returns.

Alignment of receptor nomenclature with the human genome: classification of 5-HT1B and 5-HT1D receptor subtypes

1996 ◽  
Vol 17 (3) ◽  
pp. 103-105 ◽  
Author(s):  
Paul R. Hartig ◽  
Daniel Hoyer ◽  
Patrick P.A. Humphrey ◽  
Graeme R. Martin
Keyword(s):  
Human Genome ◽  
Receptor Subtypes

scholarly journals Intergenic, gene terminal, and intragenic CpG islands in the human genome

BMC Genomics ◽  
2010 ◽  
Vol 11 (1) ◽  
pp. 48 ◽  
Author(s):  
Yulia A Medvedeva ◽  
Marina V Fridman ◽  
Nina J Oparina ◽  
Dmitry B Malko ◽  
Ekaterina O Ermakova ◽  
...  
Keyword(s):  
Human Genome ◽  
Cpg Islands

scholarly journals CpGIMethPred: computational model for predicting methylation status of CpG islands in human genome

2013 ◽  
Vol 6 (Suppl 1) ◽  
pp. S13 ◽  
Author(s):  
Hao Zheng ◽  
Hongwei Wu ◽  
Jinping Li ◽  
Shi-Wen Jiang
Keyword(s):  
Computational Model ◽  
Human Genome ◽  
Methylation Status ◽  
Cpg Islands

A CpGCluster-Teaching–Learning-Based Optimization for Prediction of CpG Islands in the Human Genome

2018 ◽  
Vol 25 (2) ◽  
pp. 158-169 ◽  
Author(s):  
Cheng-Hong Yang ◽  
Yi-Cheng Chiang ◽  
Li-Yeh Chuang ◽  
Yu-Da Lin
Keyword(s):  
Human Genome ◽  
Cpg Islands ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

Compositional searching of CpG islands in the human genome

2005 ◽  
Vol 71 (6) ◽  
Author(s):  
Pedro Luis Luque-Escamilla ◽  
José Martínez-Aroza ◽  
José L. Oliver ◽  
Juan Francisco Gómez-Lopera ◽  
Ramón Román-Roldán
Keyword(s):  
Human Genome ◽  
Cpg Islands
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