A Survey on Identification of Motifs and Ontology in Medical Database

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 B. Lavanya*, T. Madhumitha

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.  [2], describes the network motif for coronary artery disease. Differential integrated gene and protein-protein interaction gene are analyzed to interaction pattern is identified by screening of differential network. The network is to find the top 20 network, which is used to identify the coronary artery disease. For screening the network the R package global ancova software where used. The main advantage of screened network motif is, to give the accurate result to identify the coronary artery disease. This network motif method gives the accurate result.
Yin Wang et. al. (2016), [3], classify the disease based on microbial metagenome. These classifications are done by the method Phylogenetic tree based motif finding algorithm (PMF). The PMF algorithm has three parts that is motif finding, motif sorting and model evaluations. This PMF classifies two diseases, pneumonia and dental caries based on the microbial meta-genome. The main advantage of using PMF is to find the motifs in the training data, from which disease is classified. Ian Fox et. al. (2016), [4], in this system, contextual motifs for Continuous Glucose Monitor (CGM) of type1 diabetes are identified. CGM is done based on the spikes in waves, from which glucose level is monitored. The two stage discovery method with data driven contextual motifs and join discovery method with generating contextual motifs are used. The first method is expert drive method, used by the domain expert and data driven is an unsupervised method. Join discovery method is used to identify, how long the patient suffered from diabetes by long term or short term event, and it uses AUC curve. The main advantage based on contextual motifs is, to identify hypo and hyper glycemic event.
Sakorn Mekruksavanich (2016), [5], discuss to diagnose the disease based on the diabetes ontology. This diagnose is done through the Medical Expert System. Ontology is done by sub-classes and in those subclasses the relationship is analyzed. The fuzzy logic is appeared for diagnose the disease by disease risk factor. The weighted similarity algorithm is used to show the result of the diagnosis. The proposed system is used for diabetes diagnosis. And the main advantage is, this is a web based application. So, the user can easily interface with this application and to get the best advice for the disease diagnosis.
Charles , used the method called network motifs, the centrality for analysing the shortest path between the nodes. The highest the centrality scores the more significant motifs. This is the application based on colorectal cancer disease. The pathway in the disease, it is a significant pathway which enriches the gene reported related to cancer development. Giuseppe Agapito et. al. (2016), [16], describe the method Gene Ontology Based Weighted Association Rule (GO-WAR) for ontology based annotation dataset. The GO-WAR use the Weighted Support and Confident, from which the analysis of research is better. The Performance is measured by memory consumption and execution time. This process is used as software tool to analyse the gene ontology.
Adnan Ferdous Ashrafi et. al. (2015), [17], which will find the motifs in DNA sequence by Integer Matching using Hash table indexing, and rank the motifs then calculate the fitness in DNA sequence. The main advantage is the DNA sequence will be accurate and effective.
J. Sivaranjani et. al. (2017), [18], which describes the motifs in medical ECG data and the performance of motif measured by F-Measures. And this motif is discovering time series method. This application uses the Hadoop environment to discover the motifs in ECG data.

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.