Reconstruction of Gene Regulatory Network using Modified Ant-based Algorithm
Rakhi Wajgi1, Manali Kshirsagar2, Dipak Wajgi3, Gauri Chaudhary4, Gauri Dhopavkar5
1Rakhi Wajgi*, Computer Technology Department, YCCE, Nagpur, Maharashtra, India.
2Manali Kshirsagar, Principal RGCER, Nagpur, Maharashtra , India.
3Dipak Wajgi, Computer Engineering Department, SVPCET, Nagpur, Maharashtra, India.
4Gauri Chaudhary, Computer Technology Department, YCCE, Nagpur, Maharashtra, India.
5Gauri Dhopavkar, Computer Technology Department, YCCE, Nagpur, Maharashtra, India.
Manuscript received on March 05, 2020. | Revised Manuscript received on March 16, 2020. | Manuscript published on April 30, 2020. | PP: 443-448 | Volume-9 Issue-4, April 2020. | Retrieval Number: C6467029320/2020©BEIESP | DOI: 10.35940/ijeat.C6467.049420
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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: Healthcare is a major area of research since few years. Ample amount of biological data getting accumulated daily due to advancement in technologies. Microarray is such technology which captures expressions of thousands of genes at a time. Interactions occur among genes are represented in terms of special networkeknown as Gene Regulatory Network (GRN). It is constructed from Differentially Expressing Genes(DEFs). GRN is a graphical representation containing genes as nodes and regulatory interactions among them as edges. It helps in tracking pathways where usual gene interaction changes leading to malfunctioning of cells and results in illness. Also, now a day’s people are diagnosed with new diseases like dengue, swine flu, Nipah, Corona virus infection for which exact molecular pathways are yet to be invented through GRN. Therefore, in this paper, a nature inspired algorithm is used for reconstruction of GRN using differentially expressing genes.
Keywords: Microarray, Genes, Cellular Biology, Gene Regulatory Network, Differentially Expressing Genes