GPKB- for Gene Disease Identification and Medical Diagnosis using MF, CC, BF, Micro RNA and Transcription Factors
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Publication date: 2019-11-25
Eurasian J Anal Chem 2018;13(3):em2018153
Multiple genomic and proteomic semantic annotations scattered in many distributed and heterogeneous data sources; such heterogeneity and dispersion hamper the biologists’ ability of asking global queries and performing global evaluations. To overwhelm this problem, we developed a software planning to create and maintain a Genomic and Proteomic Knowledge Base (GPKB), which integrates several of the most relevant sources .Gene Ontology (GO) is a structured repository of concepts that are associated to one or more gene products through a process referred to as annotation. There are different method of analysis to get bio information. One of the method is the use of Association Rules (AR) which discovers biologically applicable associations between terms of GO. In existing work we used GO-WAR (Gene Ontology-based Weighted Association Rules) for extracting Weighted Association Rules from ontology- based annotated datasets. We here adapt the MOAL algorithm to mine cross-ontology association rules, i.e. rules that involve GO terms present in the three sub- ontologies of GO. We are proposing cross ontology to manipulate the Protein values from three sub ontologies for identifying the gene attacked disease. Also our proposed system, focus on intrinsic and extrinsic. Based on cellular component, molecular function and biological process values intrinsic and extrinsic values would be calculated. For each proteomic analysis for every gene disease, we analyze OMIM id, disease caused by, associated genes, medicine if available, and images of that particular gene disorder. Thus a common man also would be able to understand the membranes and enzymes associated for his / her gene disorder and able to identify intrinsic and extrinsic factors. In this Paper, We done the Co-Regulatory modules between miRNA (microRNA), TF (Transcription Factor) and gene on function level with multiple genomic data.. We compare the regulations between miRNA-TF interaction, TF-gene interactions and gene-miRNA interaction with the help of integration technique. These interaction could be taken the genetic disease like breast cancer, etc.. Iterative Multiplicative Updating Algorithm is used in our paper to solve the optimization module function for the above interactions. After that interactions, we compare the regulatory modules and protein value for gene and generate Bayesian rose tree for efficiency of our result.