Relationship Identification & Prediction of Diseases Association using Micro-RNA of Genomic Data
C Nalini1, S. Amudha2, S. Sangeetha3
1Dr. C Nalini, Professor, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai, Tamilnadu, India
2Ms. S. Amudha, Department of CSE, Bharath Institute of Higher Education and Research, Chennai, Tamilnadu, India.
3Ms. S. Sangeetha, Department of CSE, Bharath Institute of Higher Education and Research, Chennai, Tamilnadu, India.
Manuscript received on 07 July 2019 | Revised Manuscript received on 19 July 2019 | Manuscript Published on 23 August 2019 | PP: 1093-1096 | Volume-8 Issue-9S3 August 2019 | Retrieval Number: I32350789S319/2019©BEIESP | DOI: 10.35940/ijitee.I3235.0789S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The current process of finding the relationship between the father and the son and also predicting the diseases that is yet to occur is quite inaccurate because it includes only the gene-id of the respected person. In order to handle or to make this system more accurate, we propose this system by using the chromosome structure of the person. This system takes the input of the chromosome structure of the son that has been partitioned from the father’s chromosome structure. It initially preprocesses the image of the son using the collaborative filtering for making it look different from the input image to show the similarity between the father and the son. It then detects the edge of the structure after preprocessing it using the SOBEL edge detection algorithm. The SOBEL edge detection algorithm is that the gradient of the image is calculated for each pixel position in the image. After detecting the edges of those input images, matching process starts between the input image and the list of father chromosome images. Then the matched output appears. In order to predict the diseases which is yet to come in future for the son is represented graphically by dividing it into three colors, firstly green represents there is less possibility of the son getting the disease, secondly yellow represents there may be any chance of son getting the disease and finally red represents there is high possibility of son getting the disease.
Keywords: Image processing, Edge detection, Image matching, Graphical representation
Scope of the Article: Signal and Image Processing