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Prediction of Human Genetic Disease based on Guanine - Cytosine Count

Annwesha Banerjee1 , Anindya Sundar De2 , Rashbihari Halder3 , Gopal Basak4 , Agnish Majumder5

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-2 , Page no. 528-531, Feb-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i2.528531

Online published on Feb 28, 2019

Copyright © Annwesha Banerjee, Anindya Sundar De, Rashbihari Halder, Gopal Basak, Agnish Majumder . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Annwesha Banerjee, Anindya Sundar De, Rashbihari Halder, Gopal Basak, Agnish Majumder, “Prediction of Human Genetic Disease based on Guanine - Cytosine Count,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.528-531, 2019.

MLA Style Citation: Annwesha Banerjee, Anindya Sundar De, Rashbihari Halder, Gopal Basak, Agnish Majumder "Prediction of Human Genetic Disease based on Guanine - Cytosine Count." International Journal of Computer Sciences and Engineering 7.2 (2019): 528-531.

APA Style Citation: Annwesha Banerjee, Anindya Sundar De, Rashbihari Halder, Gopal Basak, Agnish Majumder, (2019). Prediction of Human Genetic Disease based on Guanine - Cytosine Count. International Journal of Computer Sciences and Engineering, 7(2), 528-531.

BibTex Style Citation:
@article{Banerjee_2019,
author = {Annwesha Banerjee, Anindya Sundar De, Rashbihari Halder, Gopal Basak, Agnish Majumder},
title = {Prediction of Human Genetic Disease based on Guanine - Cytosine Count},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {2 2019},
volume = {7},
Issue = {2},
month = {2},
year = {2019},
issn = {2347-2693},
pages = {528-531},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3699},
doi = {https://doi.org/10.26438/ijcse/v7i2.528531}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i2.528531}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3699
TI - Prediction of Human Genetic Disease based on Guanine - Cytosine Count
T2 - International Journal of Computer Sciences and Engineering
AU - Annwesha Banerjee, Anindya Sundar De, Rashbihari Halder, Gopal Basak, Agnish Majumder
PY - 2019
DA - 2019/02/28
PB - IJCSE, Indore, INDIA
SP - 528-531
IS - 2
VL - 7
SN - 2347-2693
ER -

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Abstract

Through the proposed method GC content of human DNA sequence have been calculated. The GC content plays a major role in disease prediction. Normally in a human genome the GC content is 35% to 60% , if found less than 35% then it indicates about some deficiency diseases like essential amino acid deficiency disease ( mainly Alanine, proline, glycine); and if this content is found more than 60%, then it can be indication of some chromosomal or genetic diseases. So, based on the report of GC content a human can take some precautions to eradicate the probability of happening these kind of diseases.

Key-Words / Index Term

Alanine, Cytosine, DNA, Guinine, Glycine, Proline

References

[1] Baldi P and Brunak S (1998) Bioinformatics: The Machine Learning Approach, MITPress, Cambridge,MA.
[2] lazier AM, Nadeau JH & Aitman TJ (2002) Finding genes that underlie complex traits. Science 298 , 2345– 2349.
[3] Botstein D & Risch N (2003) Discovering genotypes underlying human phenotypes: past successes for Men- delian disease, future approaches for complex disease. Nat Genet 33 , 228–237.
[4] Spencer G. International Consortium Completes Human Genome Project. Bethesda, MD: National Institutes of Health, 2003.
[5] Collins FS, Green ED, Guttmacher AE, Guyer MS, U.S. National Human Genome Research Institute. A vision for the future of genomics research. Nature 2003;422:835– 47.
[6] Bell J. Predicting disease using genomics. Nature 2004;429:453– 6.
[7] 7. Roses AD. Pharmacogenetics and the practice of medicine. Nature 2000;405:857.
[8] Williams RSaG.-C PJ. The genetics of cardiovascular disease: from genotype to phenotype. Dialogues in Cardiovascular Medicine 2004; 9:3–19.
[9] Guttmacher AE, Collins FS. Genomic medicine—a primer. N Engl J Med 2002;347:1512–20.
[10] Cook SA, Rosenzweig A. DNA microarrays: implications for cardiovascular medicine. Circ Res 2002;91:559 – 64.
[11] Goldsmith ZG, Dhanasekaran N. The microrevolution: applications and impacts of microarray technology on molecular biology and medicine (review). Int J Mol Med 2004;13:483–95.
[12] Napoli C, Lerman LO, Sica V, Lerman A, Tajana G, de Nigris F. Microarray analysis: a novel research tool for cardiovascular scientists and physicians. Heart 2003;89:597– 604
[13] van de Vijver MJ, He YD, van’t Veer LJ, et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002;347:1999 –2009.
[14] Bild AH, Yao G, Chang JT, et al. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 2006;439: 353–7.
[15]. Berchuck A, Iversen ES, Lancaster JM, et al. Patterns of gene expression that characterize long-term survival in advanced stage serous ovarian cancers. Clin Cancer Res 2005;11:3686 –96.
[16] Patino WD, Mian OY, Kang JG, et al. Circulating transcriptome reveals markers of atherosclerosis. Proc Natl Acad SciUSA 2005;102: 3423– 8.
[17] Saunders, M., Lewis, P. and Thornhill, A. (2012) Research Methods for Business Students. Pearson Education Ltd., Harlow.