Biomedical Knowledge Base for Genomic & Proteomic Analysis Using Graph
Gene Analysis Graph Theory
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Image segmentation is emerging as an intriguing and throught provoking topic in the field of Computer Science and Engineering. Density based clustering algorithm has been used for image segmentation in this research. Gene analysis has a huge scope in identifying the genetic disorders early and preform the respective diagnosis. Gene regulatory modules microRNA (miRNA) and transcription factor (TF) play a very important role in gene regulation. Moreover, Clustering is a main challenge in gene analysis. This challenge reflects a huge effect on genetic field. Thus in existing system the multiple g...
Image segmentation is emerging as an intriguing and throught provoking topic in the field of Computer Science and Engineering. Density based clustering algorithm has been used for image segmentation in this research. Gene analysis has a huge scope in identifying the genetic disorders early and preform the respective diagnosis. Gene regulatory modules microRNA (miRNA) and transcription factor (TF) play a very important role in gene regulation. Moreover, Clustering is a main challenge in gene analysis. This challenge reflects a huge effect on genetic field. Thus in existing system the multiple genomic and proteomic analysis are scattered in multiple distributed systems. In this research, a common knowledge base for genomic and proteomic analysis using graph clustering, collaborative filtering (CF) and Depth First Search (DFS) is developed to group the genes and regulatory modules for each and every gene expression. Finally, the challenge of deriving taxonomy for a particular gene id is resolved using Bayesian Rose Tree (BRT). The main aim of the research is to serve for the medical industry by combining the image segmentation technology and gene ontology.
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