Biomedical Informatics

“Never in the history of mankind have we had access to so much medical data – electronic health records, medical imaging, “omic” data – the challenge now is turning this data deluge into meaningful insights.  This is where data scientists step in.  The demand for biomedical informatics specialists far exceeds supply.  We are witnessing the largest transformation of healthcare ever – bioinformatics specialists will be the rock stars of tomorrow.”

- Don Aldrige, Executive Director of the Centre for Advanced Computing & Senior Advisor of Advanced Computing and Data Analytics

Transforming how health care is approached and delivered through big data is the goal of our two new professional programs: a graduate diploma and a master's in Biomedical Informatics.

Using a ladder approach, students can take the 4 month graduate diploma, with the option to continue on to complete a one-year masters. Skills to be gained in these programs will provide hands-on training in data science that will form the foundation for successful careers in health care and biomedical research. Given the current abundance of data, knowledge and experience in data analytics is in high demand among health care professionals and researchers. Whether you are interested in pursuing careers in genetics, pharmaceuticals, medicine, or biomedical research, understanding how to manipulate and use large datasets is essential for translating data into knowledge that will undoubted transform health care.

Innovative in design, offered in partnership by the School of Computing and the Department of Biomedical and Molecular Sciences (DBMS), students will succeed through the hands-on and applied nature of the program. These programs are focused on training future data scientists who have a foundation in biology, life sciences, biochemistry, medical sciences and related disciplines in methods for database design and management, statistical analysis, data mining, and image analysis.

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Details about the Graduate Diploma

May - August (4 months)

$10,390 CDN (Domestic Students)
$21,000 CDN (International Students)

on campus

Citizens of some countries will require a temporary resident (entry) visa to be allowed to come to Canada. The Queen's University International Centre (QUIC) offers information about applying for Canadian Permits and Visas

The diploma consists of 4, 3-credit units including a final paper for CISC-897. The courses are:

  • CISC-897* (3 credit units) – Research Methods in Computer Science
  • BMIF-801* (3-credit units) – Programming Skills and Tools for Processing of Biomedical Data
  • BMIF-802* (3-credit units) – Biomedical Data Analysis
  • BMIF-803* (3-credit units) – Biomedical Data Mining and Applications

Details about the Master's Program (MBI)

May - May (12 months)

$20,780 CDN (Domestic Students)
$42,000 CDN (International Students)

on campus

International students participating in programs longer than 6 months in length are required to have a study permit issued by the Canadian government. Additionally, citizens of some countries will require a temporary resident (entry) visa to be allowed to come to Canada. The Queen's University International Centre (QUIC) offers information about applying for Canadian Permits and Visas

Students in the Diploma program will be permitted to ladder coursework successfully completed within the Diploma program into the Professional Master’s program. The program will consist of:

  • The equivalent of 24 credit unit courses, including those from the Graduate Diploma Program (12CU) as well as advanced courses in computing, biology and health science.
  • CISC 898 Master’s Project (6CU). A biomedical informatics project is undertaken under the supervision of a School member. The presentation of a seminar to describe the project is required.
  • Two additional 3 credit unit courses from a list of elective courses presented below (total: 6CU).

Elective course options include:

  • BMIF-898 Master's Project
  • CISC-832 Data Base Management Systems
  • CISC-859 Pattern Recognition
  • CISC-873 Data Mining
  • CISC-881 Bioinformatics
  • CISC-886 Cloud Computing
  • BMED-809 Principle of Drug Discovery and Development
  • BMED-810 Protein Structure and Function
  • BMED-811 Advanced Molecular Biology
  • BMED-813 Advances in Neuropharmacology
  • BMED-815 Mechanistic Toxicology
  • BMED-854 Cardiovascular Sciences

Program Professors and Faculty

DBMS and the School of Computing are partnering in the development of these graduate programs in Biomedical Informatics. These opportunities will fill gaps in training highly qualified personnel with the skill set to apply computer science to multiple biological and medical datasets, including genomics and imaging, to uncover, understand and integrate biological relationships to better understand the biology underlying human diseases or traits such as drug response.

Core faculty from Computing include:
Dr. Qingling Duan (Queen’s National Scholar in Bioinformatics) is jointly appointed to the Department of Biomedical and Molecular Sciences and the School of Computing
Randy Ellis
(Queen’s Research Chair in Computer-Aided Surgery)
Gabor Fitchtinger (Cancer Care Ontario Research Chair)
Parvin Mousavi (Medical Informatics Lab)
James Stewart (Computer Assisted Mosaic Arthroplasty (CAMA))
Selim Akl (Parallel and Unconventional Computation Group)
Dorothea Blostein (Biomechanics and Adaptive Tensegrity)

The School of Computing is currently a leader in Biomedical Computing, offering a unique undergraduate program in the field and carrying out world-class research in the areas of bioinformatics, computer-aided surgery, medical imaging, computer-assisted intervention, medical diagnosis, drug discovery, and computational neuroscience. External collaborations include researchers at the National Institute of Health (USA), Harvard University, John Hopkins University, Medialo University of Vienna, University of British Columbia, Western University, Princess Margaret Hospital (Toronto), and others. In addition, the School of Computing has experts in such related areas as big-data analysis (Pat Martin) and data mining (David Skillicorn).


Core faculty from DBMS will include:
Dr. Qingling Duan (Queen’s National Scholar in Bioinformatics) is jointly appointed to the Department of Biomedical and Molecular Sciences and the School of Computing
Michael Adams
(Head of the Department)
James Reynolds
Gunnar Blohm
Christopher Mueller
Louise Winn

DBMS is a distinguished academic centre engaged in a wide range of research endeavours including anatomical sciences, bacteriology, biochemistry, cancer biology, cardiovascular sciences, cell biology, developmental biology, immunology, molecular biology, genomics, neuroscience, pharmacology, physiology, reproductive biology, toxicology and virology. The breadth and depth of research has a strong foundation in multi-disciplinary discovery. Prominent in this regard are the educational efforts in the highly regarded Life Sciences and Biochemistry undergraduate programs, in medical education and in stellar graduate program. 

Course Descriptions

CISC-897* Research Methods in Computer Science (3CU)
Provides incoming students with basic research and working skills, facilitating a smoother transition to graduate studies and research. The course spans multiple elements including time management, writing and presentation skills, and general considerations for experiment design and planning.

BMIF-801* Programming Skills and Tools for Processing Biomedical Data (3CU)
Provides students with hands-on training in computer programming languages, software tools and algorithms used in biomedical research. Topics will include an introduction to programming in R and MATLAB, pre-processing and management of biomedical datasets, identification of outliers, workflow for quality control assessments, and basics of cloud computing. Examples of real biomedical datasets will be provided to illustrate the application of programming tools. Prerequisites for this course include admission in the Graduate Diploma (GDip [BI]) or permission of the Course Instructor.

BMIF-802* Biomedical Data Analysis (3CU)
Provides students with hands-on training in analysis of biomedical datasets. Topics  will include feature extraction and classification, pattern recognition, supervised and unsupervised learning, and basic concepts of biostatistics as applied to the analysis of biomedical data. Examples of real biomedical datasets will be provided to demonstrate various methodologies for data analysis. Prerequisites for this course include BMIF 801 as well as admission in the Graduate Diploma (GDip [BI]) or permission of the Course Instructor.

BMIF-803* Biomedical Data Mining and Applications (3CU)
Provides students with hands-on training in data mining and its applications in various areas of biomedical research. Topics will include the uses of data mining and the application of various tools using real biomedical data. Examples of real biomedical datasets will be provided to demonstrate various methodologies for data mining and relevance to biomedical research. Prerequisites for this course include BMIF 801 and 802 as well as admission in the Graduate Diploma (GDip [BI]) or permission of the Course Instructor.

BMIF-898 Master's Project

A project is chosen after consulting with potential supervisors. The project should normally require between two and four months of full time work, and be comparable in work load to two CISC graduate level half courses. A written report of the project is submitted by the student and is then independently evaluated by each member of a committee of three. The committee consists of the supervisor, a School examiner, and the Director (or delegate). The ultimate authority for appointing members of the committee lies with the Director. The chair of the committee, who is allowed to vote, is the Director (or delegate). All outcomes of the project examination require at least two votes. If the initial decisions are not all in agreement then the project should be discussed before an outcome is decided.

There are three possible outcomes of the project examination, PASSED, REFERRED, and FAILED. These are patterned on the decisions of an MSc thesis exam, and are detailed in the Calendar of the School of Graduate Studies and Research section 8.6 paragraph b. A project is PASSED if it is acceptable in its present form or requires minor revisions. In the case of minor revisions, the supervisor is responsible for informing the student of the required changes and for verifying that the changes have been made. A project is REFERRED if it is not acceptable in its present form, but could be acceptable pending major revisions. The chair, in consultation with the committee, communicates a list of revisions to the student in writing. A copy of this letter will be sent the Graduate Coordinator. The revised project is then re examined by the entire committee and can either be PASSED or FAILED. The third outcome of a project examination is FAILED. In the case that a project is failed, the student must withdraw from the program. In all cases the outcome of the project should be decided and communicated to the student, as well as the graduate coordinator, at most two weeks (ten working days) after every member of the committee has received it.

CISC-832* Data Base Management Systems (3CU)
The course examines advanced topics in database management systems. The course will first examine the implementation of relational DBMSs including query processing, query optimization, concurrency control and recovery. The course will then cover topics related to big data such as data warehouses, parallel and distributed architectures, column-oriented data stores, streaming data and NoSQL systems.

CISC-859* Pattern Recognition (3CU)
An introduction to statistical and structural pattern recognition. Feature extraction and the feature space. Bayes decision theory. Parametric classification. Clustering methods. Syntactic pattern description: string, tree and graph grammers; attributed grammars; stochastic grammars. Error correcting parsing; parsing of stochastic languages. Assignments include practical experience in application areas such as character recognition and document image analysis. Three term-hours; lectures and seminars.

CISC-873* Data Mining (3CU)
In this course students learn to build models of complex systems from data collected about them. Different modelling techniques are used by different students to address the same datasets, and results compared in class.

CISC-881* Bioinformatics (3CU)
The overall objectives of the course are to discuss the design and structure of computer-assisted intervention systems; their subsystems and their interdependence. In particular, we discuss surgical navigation systems; medical imaging; coordinate system registration; position tracking; visualization; intervention planning.

CISC-886* Cloud Computing (3CU)
The goal of the courses is to introduce students to key concepts and techniques from cloud computing. The course focuses on issues such as system architectures, resource allocation and management, and approaches and systems for the storage, management and processing of data in cloud environments.

BMED-809* Principles of Drug Discovery and Development (3CU)
BMED 809 is a problem-based course focusing on and consisting of discussions of receptor theory, mechanisms of drug action, drug metabolism, pharmacokinetics, pharmacogenetics and pharmacogenomics, and drug transport. The course comprises lectures, problem-solving discussions and seminars, based on recent literature.

BMED-810* Protein Structure and Function (3CU)
This course presents an integrated approach to the study of protein functions in vivo and in vitro. Topics include global (proteomics) as well as small-scale analyses of protein interactions.Theories  and  applications  of  a selection  of  techniques  will  be  taught,  such  as  mass  spectrometry, 2-dimensional gel electrophoresis, fluorescence microscopic imaging, surface plasmon resonance, calorimetry, bioinformatics and protein evolution, protein modifications and processing, interpretation and applications of 3-D structure, and structure-function relationships.

Hands-on trainings on selected techniques will be provided to students, who will give short presentations  of what  they  have  learned  to  the  class  during  the  last  two  weeks  of  the  term.

BMED-811* Advanced Molecular Biology (3CU)
This course focuses on advanced molecular biology as it applies to the human genome and human diseases. Topics that will be covered include: Genomics and genetic variation, SNPs, pharmacogenomics, forensics, recombination, GWAS, Next Generation Sequencing, microbiome, regulation of gene expression, X-inactivation, stem cells regenerative medicine. Bioinformatics including the use of UCSC genome browser, HapMap browser and Ingenuity pathway analysis. The course will build from concepts learned in BCHM218, and will extend to current literature and review articles.

BMED-813* Advances in Neuropharmacology (3CU)
Recent advances in understanding neurotransmission and pharmacology in the central nervous system will be discussed. The current literature describing progress in understanding molecular, cellular and behavioural aspects of brain function, and the impact of drugs and disease, will be examined. Winter; seminars and tutorials. Given in years ending with an uneven number.

BMED-815* Mechanistic Toxicology (3CU)
A problem-based course consisting of discussions of major mechanisms of toxicity supplemented with student presentations of research articles.

BMED-854* Cardiovascular Sciences (3CU)
A study of the physiology, pharmacology and anatomy of the cardiovascular system. Topics include integrative mechanisms and pharmacotherapy involved in short-term and long-term control of the circulation in health and disease.


Opportunities and Career Paths for Grads


Queen’s offers its students a perfect balance of engagement with rigorous academic programs and access to first class practitioners and learning facilities.  Both the diploma and M.A. programs are cross-disciplinary -- taught by a combination of instructors from the School of Computing and the Faculty of Health Sciences.

Career paths – employment opportunities

  • Chief Information Officers (CIOs)
  • Chief Medical Information Officers (CMIOs)
  • Chief Medical Officers (CMOs)
  • Chief Nursing Information Officers (CNIOs)
  • Biomedical and clinical research, including the application of genomics and molecular biology
  • Clinical Informaticist
  • Consumer health and Public health
  • Imaging
  • Directors of Medical Informatics
  • Project managers & designers
  • Researchers
  • Programmers  & analysts
  • Health information technology (HIT) educators, trainers, consultants
  • Medical / technical writers
  • Nursing informatics specialists
  • Account representatives

The following examples of the workplace environments for our graduates:

  • Academic institutions
  • Community health centers
  • Consultanting agencies
  • eHealth companiesHospitals and health systems
  • Physician practices and clinics
  • Health care agencies within federal and state government
  • Health information technology system vendors
  • Health insurance companies
  • Pharmaceutical companies

Our City - Kingston Ontario Canada

We're proud to say that not only are we an official "Intelligent Community" but that the BBC named Kingston as one of the greatest university towns in the world. Kingston is a home-away-from-home not only for students from Queen’s, but also for those attending the Royal Military College and St. Lawrence College - nearly 30,000 in all! We are a student town. We are in the middle of everything - Montreal, Toronto, Ottawa and even New York - all less than a day trip away. Instagram also ranked us the happiest city on the planet. So why not learn alongside the smartest, happiest people around?

Learn more about Kingston

Application Process

To be considered for admission to the Graduate Diploma or the MBI, an applicant must hold a minimum of a BSc (Honours) degree in biology, life sciences, biochemistry, medical sciences, computer science, biostatistics, engineering, and related disciplines, who are interested in designing and implementing quantitative and computational methods that solve challenging problems across the entire spectrum of biology and medicine, and who wish to develop the skills required for a range of exciting careers in medicine, research and development, or industry. The minimum acceptable average for admissions to these programs is B+ in the third and fourth years of the student’s undergraduate program (all courses considered). Although the program is aimed at recent graduates from undergraduate programs, applicants from professional programs such as medicine and nursing are also welcome.

Students applying from outside of North America whose native language is not English are required to submit TOEFL scores. International students participating in programs longer than 6 months in length are required to have a study permit issued by the Canadian government. Additionally, citizens of some countries will require a temporary resident (entry) visa to be allowed to come to Canada. The Queen's University International Centre (QUIC) offers information about applying for Canadian Permits and Visas

Important Dates & Deadlines
Deadline to Apply (for both programs): February 1, 2020
Offer of Admissions to begin: February 1, 2020

We will consider applications after February 1 as long as space remains in the program.

How-to Apply