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Swinburne University of Technology Sarawak Campus

Dr. Yakub Sebastian

Discipline Leader – BICT, Lecturer
BBus (Hons) (Swinburne), MSc (Swinburne), PhD (Monash), MACS CP

Faculty of Engineering, Computing and Science

Office No:+60 82 260 677
Fax No:+60 82 260 813
Room No: E607, Building E, FECS
Email: ysebastian@swinburne.edu.my

Biography

Yakub Sebastian received his BBus (Hons) in Information Systems from Swinburne University of Technology Sarawak Campus in 2009 and later his MSc in Computer Science from the same university in 2012. He earned his doctoral degree from Monash University in 2017, working in the area of literature-based discovery. 

His current research interests include data science, literature-based discovery, healthcare data analytics, and network science.

Research Interests

  • Data science
  • Literature-based discovery
  • Healthcare data analytics
  • Network science

PhD/Master by Research Opportunity

Potential research higher degree candidates are welcome to enquire about postgraduate opportunities in the following areas/ projects:

  1. Healthcare data analytics
  2. Literature-based discovery

Please contact Dr. Yakub Sebastian for more information on the above topics.

Current RHD Supervision

NameDegreeArea of ResearchStart yearRole
Jason Thomas ChewMScIntelligent health informatics2017Co-supervisor

  • Publications

Publications

  • Journal paper Chew, J. T., Sebastian, Y., Raman, V., Tiong, X. T., Fong, A. Y. Y. & Then, P.H. H. (2018). GLADIATER: An algorithm to support anomaly detection from a longitudinal cardiovascular and diabetes dataset. Journal of the American College of Cardiology, 72:16 (Suppl.), pp.C244-C245 (Oct 2018).
    http://www.onlinejacc.org/content/72/16_Supplement/C244.3
  • Journal paper Sebastian, Y., Tiong, X. T., Raman, V., Fong, A. Y. Y. & Then, P. H. H. (2017). Advances in Diabetes Analytics from Clinical and Machine Learning Perspectives. International Journal of Design, Analysis & Tools for Integrated Circuits & Systems, 6:1, pp.32-37 (Oct 2017).
    http://ijdatics.datics.net/current_issues/IJDATICS_06_01/IJDATICS_06_01_09.pdf
  • Journal paper Sebastian, Y., Siew, E. G. & Orimaye, S. O. (2017). Emerging approaches in literature-based discovery: techniques and performance review. The Knowledge Engineering Review, 32, e12 (May 2017). Cambridge University Press.
    https://doi.org/10.1017/S0269888917000042
  • Journal paper Sebastian, Y., Siew, E. G. & Orimaye, S. O. (2017). Learning the heterogeneous bibliographic information network for literature-based discovery. Knowledge-Based Systems, 115, pp. 66-79 (Jan 2017). Elsevier.
    https://doi.org/10.1016/j.knosys.2016.10.015 
  • Journal paper Sebastian, Y. and Then, P. H. H. “Domain-driven KDD for mining functionally novel rules and linking disjoint medical hypotheses”. Knowledge-Based Systems Vol.24, No.5, pp 609-620, 2011.
  • Journal paper Lai, L.Y.H., Siti Nadiah, R., Tiong, L.L., Sahimi, M., Fong,  A.Y.Y.,  Sebastian, Y., Yanti, N.S., Sim, K.H. and Sim, K.H. “The impact of pharmacist-initiated interventions in improving Acute Coronary Syndrome (ACS) secondary prevention pharmacotherapy prescribing upon discharge”. International Journal of Pharmacy Practice, Vol.19, Suppl. 2, pp 50-51, 2011.
  • Conference paper Sebastian, Y., Chew, J.T., Tiong, X. T., Raman, V., Fong, Alan Y. Y. & Then, Patrick H. H. (2018) Anomaly detection from diabetes similarity graphs using community detection and Bayesian techniques. In Proceedings of the 12th International Conference on Ubiquitous Information Management and Communication (ACM IMCOM ’18), Langkawi, Malaysia, 5-7 January 2018. ACM. https://doi.org/10.1145/3164541.3164643
  • Conference paper Sebastian, Y., Siew, E. G. & Orimaye, S. O. (2015) Predicting future links between disjoint research areas using heterogeneous bibliographic information network. In: Cao T., Lim EP., Zhou ZH., Ho TB., Cheung D., Motoda H. (eds), Advances in Knowledge Discovery and Data Mining. Lecture Notes in Computer Science, vol. 9078, pp. 610-621. Proceedings of the 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2015), Ho Chi Minh City, Vietnam, 19-22 May 2015. Springer. https://doi.org/10.1007/978-3-319-18032-8_48
  • Conference paper Sebastian, Y. (2014). Cluster links prediction for literature based discovery using latent structure and semantic features. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR ’14), Gold Coast, Queensland, Australia, 06-11 July 2014, pp. 1275. ACM. https://doi.org/10.1145/2600428.2610376