J. Today’s Ideas - Tomorrow’s Technol.

A Survey on Identification of Motifs and Ontology in Medical Database

B. Lavanya, T. Madhumitha

KEYWORDS

Motifs, Gene Network, Ontology Classification, Disease diagnosis, Data Mining

PUBLISHED DATE June, 2018
PUBLISHER The Author(s) 2018. This article is published with open access at www.chitkara.edu.in/publications
ABSTRACT

Motifs and ontology are used in medical database for identifyingand diagnose of the disease. A motif is a pattern network used for analysis of the disease. It also identifies the pattern of the signal. Based on the motifs the disease can be predicted, classified and diagnosed. Ontology is knowledge based representation, and it is used as a user interface to diagnose the disease. Ontology is also used by medical expert to diagnose and analyse the disease easily. Gene ontology is used to express the gene of the disease

INTRODUCTION

The motifs are identified in the medical database, which is used for diagnose the disease. These diseases are caused due to loss of cells. The network motif is sub graph which is used as biological network. The network motif can also identify various diseases like coronary artery disease (CAD), micro biota related disease, like pneumonia and dental caries. The clustering analysis of CAD is made based on gene expression. The continuous glucose level for diabetes patient are analysed through contextual motifs. From the motif in the spikes, the level of hypo and hyper glycaemia event can also be identified.

Ontology is used in medical database for disease diagnose which is web based application. Gene ontology annotation identifies the gene for clustering; the interrelation of gene is identified by ontology. Ontology will observe and explain the disease and are used for medical diagnostic system.

The mechanism of disease is important in bio-medical research and the disease can also be identified by the network motifs. The network is analysed by the interaction and association between the networks. The string representation in the ECG signal called motifs, detects normal and abnormal heart beat. SVM is used to classify the disease from the sequential outcomes of motifs. The motif mining tool is used to create the network motifs.

Page(s) 29-34
URL http://dspace.chitkara.edu.in/jspui/handle/123456789/771
ISSN Print : 2321-3906, Online : 2321-7146
DOI 10.15415/jotitt.2018.61003
CONCLUSION

From these types of method proposed, the clearly identified motifs and ontology is very helpful in medical database for identification of the disease. It helps to monitor the disease accurately and easily. From this, the domain expert can easily use the application to the disease. The motifs are used to monitor the glucose for type1 diabetes patient and the network motif used the classifier to classify the disease. Web based application will give the solution for the patient with the disease and the ontology is mainly used for the knowledge based disease prediction and it can used even by domain experts. In future work, the motifs can identified in microarray database to classify the disease by using data mining techniques.

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