A Survey on Identification of Motifs and Ontology in Medical Database


  • B. Lavanya Department of Computer Science, University of Madras, Chennai, India.
  • T. Madhumitha Department of Computer Science, University of Madras, Chennai, India.




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


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.


Download data is not yet available.


[1] R. Gupta, S.M Fayaz and S. Singh “Identification of gene network motifs for cancer disease diagnosis”, IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), Mangalore, India, pp. 13-14, 2016.
[2] Y. Li, Y. Cong and Y. Zhao, “Network motif-based for identifying coronary artery disease”, Experimental and Therapeutic Medicine, vol. 12, no.1, pp. 257-261, 2016.
[3] Y. Wang, et. al., “Motif-based text mining of microbial metagenome redundancy profiling data for disease classification”, BioMed Research International, 2016.
[4] I. Fox, et. al., “Contextual motifs- increasing the utility of motifs using contextual data”, in KDD ’17 Proceeding of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada – August 13 - 17, pp. 155 – 164, 2017.
[5] S. Mekruksavanich, “Medical expert system based ontology for diabetes disease diagnosis”, IEEE 7th IEEE International, Conference on Software Engineering and Service Science (ICESS), Beijing, China, pp. 26-28, Aug. 2016.
[6] C. C. N. Wang, Y.Lee, P. C. Y. Sheu, J. J. P. Tsai, “Application of Latent Semantic Analysis to Clustering Cardiovascular Gene Ontology”, IEEE 16th International Conference on Bioinformatics and Bioengineering, Taichung, Taiwan, pp. 363-368, 31 Oct. -2 Nov. 2016.
[7] J. Lu, D. Dai, B. Cao, Y. Yin, “Inferring human miRNA functional similarity based on gene ontology annotation”, IEEE 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNCFSKD) Changsha, China, 13 - 15 Aug. 2016.
[8] S. Padmavathi, E. Ramanujam, “Naïve bayes classifier for ecg abnormalities using multivariate maximal time series motif”, Elsevier Procedia Computer Science, vol. 47, pp. 222 - 228, 2015.
[9] Firas Zekri, Rafik Bouaziz, Emna Turki, Istanbul, “A fuzzy-based ontology for Alzheimer's disease decision support”, IEEE International Conference on Fuzzy System (FUZZIEEE), Turkey, 2-5 Aug. 2015.
[10] A. Khan, et. al., “HEPO: The hepatitis ontology for abductive medical diagnostic systems”, IEEE International Conference on Communication, Computing and Digital Systems (C-CODE), Islamabad, Pakistan, 8-9 March 2017.
[11] M. Cannataro, P. H.Guzzi and M.Milano, “GoD: An R-package based on ontologies for prioritization of genes with respect to diseases”, Journal of Computational Science, Elsevier, pp. 7-13, 2015.
[12] D. Le, V. Dang and S. B. Heidelberg, “Ontology-based disease similarity network for disease gene prediction”, Vietnam Journal of Computer science, vol. 3, no. 3, 2016.
[13] I. E. Obebode and A. Gangopadhyay, “Acquisition of diabetes-related biological associations using a motif based network: preliminary results”, IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Washington, DC, USA, pp. 9-12 , Nov. 2015.
[14] S. Ghosh, H. Nguyen and J. Li, “Predicting short-term ICU outcomes using a sequential contrast motif based classification framework”, IEEE 38th Annual International Conference of the IEEE Engineering in Medical and Biology Society (EMBC), Orlando, FL, USA, 16-20 Aug. 2016.
[15] K. Shi, L. Gao and B. Wang, “Systematic tracking of coordinate differential network motifs identifies novel disease-related genes by integrating multiple data”, Neurocomputing, vol. 206, no. c, pp. 3-12, 2016.
[16] G. Agapito, M. Cannataro, S. P. H. Guzzi and M. Milano, “Extracting Cross-Ontology Weighted Association Rules from Gene Ontology Annotations”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Mar-Apr 2016, vol. 13, no. 2, pp. 197-208.
[17] A. F. Ashrafi, A. K. M I. Newaz, R. A. Moin, M. Tanvee, M. A. Mottalib, “A modified algorithm for dna motif finding and ranking considering variable length motif and mutation” IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS), Kolkata, India, 9-11 July 2015.
[18] J. Sivaranjani and A. N. Madheswari, “A novel technique of motif discovery for medical big data using hadoop” Conference on Emerging Devices and Smart Systems (ICEDSS), Tiruchengode, India, 3-4 March 2017.




How to Cite

B. Lavanya, & T. Madhumitha. (2018). A Survey on Identification of Motifs and Ontology in Medical Database. Journal on Today’s Ideas - Tomorrow’s Technologies, 6(1), 29–34. https://doi.org/10.15415/jotitt.2018.61003