Clustering of Various Diseases by Collagen Gene Using the Positional Factor

S. Gowri (), S. Revathy, S. Vigneshwari, J. Jabez, Yovan Felix and Senduru Srinivasulu
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S. Gowri: Sathyabama Institute of Science and Technology, School of Computing
S. Revathy: Sathyabama Institute of Science and Technology, School of Computing
S. Vigneshwari: Sathyabama Institute of Science and Technology, School of Computing
J. Jabez: Sathyabama Institute of Science and Technology, School of Computing
Yovan Felix: Sathyabama Institute of Science and Technology, School of Computing
Senduru Srinivasulu: Sathyabama Institute of Science and Technology, School of Computing

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 803-809 from Springer

Abstract: Abstract Collagen Gene is a protein which is otherwise called alpha-1 type I collagen is found in human beings in type of COL1A1 encoded. The real encoding segment is type I collagen which is the fibrillar collagen. This febrile collagen includes the cartilage which is mainly found in the connectivity tissues. The diseases caused by this collagen are Osteogenesis imperfecta, Chondrodysplasias, Ehlers-Danlos Syndrome, Alport syndrome, Osteoporosis and Knobloch syndrome. These syndromes are associated according to the kinds of collagen which are from Type I to XVIII. Each kind of syndrome has respective impacts over human in which these five syndromes have real impact over the body. Clustering is one of the information mining techniques which is utilized to gather similar articles. The proposed framework is mainly utilized to assemble the significance of disease according to the phases of the collagen gene compose. This procedure of clustering is finished using the Dynamic Path Selection Clustering algorithm to assist in the prompts over the recurrence, conditional changes and its clinical significance which demonstrates the analytical factor in the gene behaviour.

Keywords: Collagen; Gene; Clustering; DPSC (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/978-3-030-41862-5_80

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