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Lecture Session Invitation: Machine Learning and AI in Biomedicine

Lecture Session Invitation:
Machine Learning and AI in Biomedicine

(Due to overwhelming responses, registration for the session is now closed.)

Professor Vladimir Brusic, Griffith University, Queensland, Australia

 

Date: 22 December 2017
Time: 6.00 pm – 7.00 pm
Location: Charles Babbage Room,
National Supercomputer Centre, Singapore, 1 Fusionopolis Way, #17-01 Connexis South

Abstract

The World’s total data is currently doubling every two years. This data expansion includes not only the
growth in quantity, but also in complexity and the types of data. The enormous rate of generation and online
access to data is profoundly changing the way how health business and biomedical research is
conducted. Biomedical data include R&D data, clinical data, activity and cost data, patient behavior data,
basic science data, and standards and ontologies, among others. Big Data approaches are increasingly
needed for utilization of results from various Omics studies and their translation into clinical practice.
These applications include predictive and content analytics for a variety of applications that support drug
discovery and optimization, the development of new diagnostic methods, and personalization of
medicine. Biomedical data vary in granularity, quality, dimensionality and complexity. There is a variety
of sources and data formats – web pages, publications, technical reports, images and graphs, tables, and
databases. The challenge is to make the transition from data to actionable knowledge. An emerging area
is the use of knowledge-based approaches for Big Data analytics whereby well-annotated data are
combined with specialized analytical tools and integrated into analytical workflows. A set of well-defined
workflow types with rich summarization and visualization capacity facilitates the transformation from
data to critical information and knowledge that enable understanding, decision making, and selection of
actions for solving various problems. Statistical and artificial intelligence methods are used as analytical
engines to make sense of rich datasets. The emerging Big Data requires dynamic integration of
standardized data into knowledge bases to make selected data sources accessible through integration
with the analytical tools. We will demonstrate the utilization of Big Data Analytics, mathematical
modeling, and artificial intelligence tools as well as challenges with two distinct examples: diagnosis of
endometriosis, and design of universal multivalent vaccines.

Biographical sketch

Dr. Brusic is a Professor at Menzies Health Institute Queensland, Griffith University, Australia. He is an
Adjunct Professor of Computer Science at the Metropolitan College, Boston University, USA and a Visiting
Professor at Kumamoto University, Japan. Dr Brusic holds BEng (Mechanical Engineering), MEng
(Biomedical Engineering), MAppSci (Info Tech), MBA, and PhD degrees. He holds a Honorary Doctorate in
Medicine from Semmelweis Medical University, Budapest, Hungary.
His previous positions include faculty or PI appointments at Harvard Medical School (Boston, USA),
University of Queensland (Brisbane, Australia), National University of Singapore, Nanyang Technological
University (Singapore), Institute for Infocomm Research (Singapore), and Walter and Eliza Hall Institute
for Medical Research (Melbourne, Australia). Dr Brusic is an Associate Editor of Frontiers in Immmunology
and an Editor of Briefings in Bioinformatics. He has published more than 200 scientific publications and
two patents. His work has attracted 12,000 citations and h-index of 47. The list of publications can be
found here: https://goo.gl/qRSmQe
His work is in the fields of Bioinformatics and Medical Informatics in immunology, cancer, medical
diagnostics, and modeling of biological systems. His research interests span the fields of Big Data analytics,
Biological Databases, computational modeling of biological systems, simulation of molecular interactions,
and biological discovery using simulation of laboratory experiments. He has developed knowledge-based
systems for biological data mining and knowledge discovery. Recently, Dr. Brusic has been developing a
new field of elemental metabolomics that focuses on the study of elements in biological and
environmental samples, transfer of elements along the food chain and environmental exposure, and their
effects on human health.

 

Jointly organized with:

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