Postgraduate Diploma in Computational Health Informatics

Purpose

The purpose of the Postgraduate Diploma in Computational Health Informatics is to prepare qualifying learners to possess the knowledge, skills and values for a biomedical science or science-related career, through a multidisciplinary, integrated approach focused on current rapidly growing "Omics" challenges in the African and international context. "Omics" refers to a field of study in biology that includes technologies and techniques associated with genomics (the study of all of a person's genes (the genome) focusing on their structure, function, evolution, mapping, and editing of genome, including interactions of those genes with each other and with the person's), transcriptomics (study of RNA transcripts that are produced by the genome, under specific circumstances or in a specific cell -using high-throughput methods, such as microarray analysis), proteomics (large-scale study of proteins, vital parts of living organisms, with many functions) and metabolomics (study of the set of metabolites present within an organism, cell, or tissue). These techniques are important for the functional interpretation of genomic data in system biology and the discovery of disease biomarkers. The understanding of these techniques will enable researchers to develop high-resolution screening and diagnostics, targeted therapies, and tools for choosing the treatment options that will work best for the patient.

The qualification will address the growing need for scientists with the quantitative skills necessary to help realise the enormous potential of sequencing-based technologies and digital Omics data science to deliver on the promise of personalized medicine to better understand, diagnose, and treat diseases.

Upon completion of the qualification, qualifying learners will be able to

• Understand the principles of the management and process of large-scale biomedical data science and apply these to one's own work, as a member of a technical team.
• Conduct Omics experiments with appropriate technical competence in a range of techniques appropriate to various Omics and that will lead to meaningful results.
• Write computer scripts and use High-Performance Computing for large-scale biomedical data.
• Formulate and test a hypothesis for digital biomedical data science to propose solutions to problems using quantitative approaches.
• Comply with laws of copyright protection and demonstrate an appropriate level of communicative competence.
• Demonstrate proficiency in fundamental analytics pipelines and methodologies in large-scale Omics data and technology towards application in diseases/drug/treatment problem-based setting in the African and global context.

Rationale

Biomedical data, including Electronic Medical Records, biomedical imaging, and "Omics", provide an opportunity to improve understanding of the mechanisms of disease and ultimately to improve human health care.

The "Omics" field has undergone major innovations and is rapidly impacting medical care across specialities. Huge advancements have been made toward storing, handling, mining, comparing, extracting, clustering, and analysing as well as visualizing big macromolecular data using novel computational approaches, machine intelligence and deep learning methods. Through High-Performance Computing (these technological innovations are allowing scientists to improve the understanding of the pathogenicity of diseases and why some individuals remain healthy while others are more susceptible to disease, and variation in treatments and response to drugs. Researchers start tackling bigger and broader questions related to population trends, variation impacting phenotypes (traits) differences, biomarker discovery, drug response/discovery, predicting and prioritizing in silico mutations leading to clinical diagnostics and personalized medical treatment of patients on a much broader scale than ever before possible with older methods.

Currently, the exposure that present and recent health sciences trainees and postgraduate learners receive in big biomedical data science relevant to human health care and variation in disease risk, and responses to drugs and treatments remains informal and inconsistent. There is a considerable gap in teaching in this field.

The challenges of promoting career entry into and closing identified skills gaps in the biosciences/biotechnology sectors are persisting. In addition, these sectors are still dominated by an ageing cohort of white males and there are few young black South (and women) African biomedical scientists in academia or industry.

Providing a qualification aligns and raises awareness of career opportunities and trajectories, how skills gained could be transferred into a range of industry careers may fill the gap and position the learners to be competitive for entry into industry or pursuing a career in biomedical science.

This qualification has been specifically designed to address the growing needs for big data Omics training for biomedical scientists and will provide fundamental quantitative skills necessary to help qualifying learners from the proposed qualification to:

• Develop and contribute to an understanding of the rapid growth of cutting-edge multidisciplinary research in biomedical data sciences, Omics technologies and fundamental quantitative skills in mining large-scale Omics data.
• Realize the enormous potential of large-scale digital sequencing-based technologies to deliver on the promise of personalized medicine to better understand the mechanisms of complex and monogenic diseases, diagnosis, prognosis, and treatment thereof.
• Foster current and future biomedical research that can provide innovative solutions to health challenges.

The introduction of this qualification is motivated by the need to strengthen the pipeline of postgraduate learners with multidisciplinary skills around large-scale biomedical data sciences, to account for the potential needs and skills required for rapidly growing large-scale "Omics" data science.

Recognition of Prior Learning (RPL)

The institution has an approved Recognition of Prior Learning (RPL) policy which is applicable with regard to equivalent qualifications for admission into the qualification. RPL will be applied to accommodate applicants who qualify. RPL thus provides alternative access and admission to qualifications, as well as advancement within qualifications. RPL may be applied for access, credits from modules and credits for or towards the qualification.

RPL for access

• Learners who do not meet the minimum entrance requirements or the required qualification that is at the same NQF level as the qualification required for admission may be considered for admission through RPL.
• To be considered for admission in the qualification based on RPL, applicants should provide evidence in the form of a portfolio that demonstrates that they have acquired the relevant knowledge, skills, and competencies through formal, non-formal and/or informal learning to cope with the qualification expectations should they be allowed entrance into the qualification.

RPL for exemption of modules

• Learners may apply for RPL to be exempted from modules that form part of the qualification. For a learner to be exempted from a module, the learner needs to provide sufficient evidence in the form of a portfolio that demonstrates that competency was achieved for the learning outcomes that are equivalent to the learning outcomes of the module.

RPL for credit

• Learners may also apply for RPL for credit for or towards the qualification, in which they must provide evidence in the form of a portfolio that demonstrates prior learning through formal, non-formal and/or informal learning to obtain credits towards the qualification.
• Credit shall be appropriate to the context in which it is awarded and accepted.

Entry Requirements

The minimum entry requirement for this qualification is

• Bachelor of Science in Computational Science, NQF Level 7.

Or

• Advanced Diploma in the related field, NQF Level 7.

Or

• A relevant qualification in the related field, NQF Level 7.

This qualification consists of the following compulsory modules at National Qualifications Framework Level 8 totalling 120 Credits.

Compulsory Modules, Level 8,120 Credits

• Omics Medicine, 15 Credits.
• Omics data Generation, Technologies/Platforms, 15 Credits.
• High-Performance Computing, 15 Credits.
• Omics Scientific Programming with Python, 15 Credits.
• Biomedical Data Analysis with R, 15 Credits.
• Web Programming and Omics Database Management, 15 Credits.
• Integrative Multi-Omics and System Biology, 15 Credits.
• Omics Research and Bio-Industry Methodology, 15 Credits.

1. Demonstrate knowledge and understanding of the principles of the management and process of large-scale biomedical data science and apply these to one's own work, as a member of a technical team.
2. Demonstrate an ability to conduct Omics experiments with appropriate technical competence in a range of techniques appropriate to various Omics and that will lead to meaningful results.
3. Demonstrate an ability to write computer scripts and use High-Performance Computing for large-scale biomedical data.
4. Demonstrate an ability to formulate and test a hypothesis for digital biomedical data science to propose solutions to problems using quantitative approaches.
5. Demonstrate an understanding of how to comply with laws of copyright protection and demonstrate an appropriate level of communicative competence.
6. Demonstrate proficiency in fundamental analytics pipelines and methodologies in large-scale Omics data and technology towards application in diseases/drug/treatment problem-based settings in the African and global context.

Associated Assessment Criteria for Exit Level Outcome 1

• Describe the principles of each omics technique and understand the principles of omics and molecular biology.
• Understand and view biology from a global perspective with respect to multi-omics (genomics, proteomics, epigenomics, metabolomics, transcriptomics, microbiomics, pharmacogenomics).
• Appreciate multi-disciplinary perspective on omics and their application to clinical diagnosis, prognosis, and therapeutic.
• Understand the mechanism of Mendelian and complex diseases.
• Describe the high-throughput platform methodologies of each Omics (genomics, proteomics, epigenomics, metabolomics, transcriptomics, microbiomics, pharmacogenomics).
• Understand each Omics laboratory protocol, and library preparation methods and choose appropriate Omics for a given clinical application.
• Appreciate and have a rigorous global view of Omics laboratory techniques, data structure and standard format.
• Appreciate the application of the various Omics technologies (genomics, proteomics, epigenomics, metabolomics, transcriptomics, microbiomics, pharmacogenomics) to improve diagnosis, monitoring, and treatment, in view of even more personalized medicine.
• Appreciate laboratory safety issues and understand/apply ethics on the usage of genetics, clinical and each Omics.

Associated Assessment Criteria for Exit Level Outcome 2

• Recognize the importance of wet laboratory safety issues, understand, and apply ethics of using genetic, and Omics information and understand the basics of confidentiality.
• Apply Linux programming and High-Performance Computing in processing, storing, and transferring big Omics data.
• Describe and use the concepts of partitioning problems for parallel computing in a big biomedical data science context.
• Describe the latest and emerging technologies in accessing the clusters and git.
• Apply parallel scripting and portable batch server to handle big Omics data analysis via Netflix and manage big collaborative biomedical studies vid High-Performance Computing.
• Appreciate the development of numerical/scientific computing and problem-solving skills and approaches through writing computer scripts with respect to big biomedical data and underlying Omics approaches.
• Apply Python programming to statistical methods for high-dimensional biomedical data analysis.
• Apply Python for computational and statistical approaches to analyse biomedical data science.

Associated Assessment Criteria for Exit Level Outcome 3

• Demonstrate proficiency in fundamental analytics pipelines and methodologies in large-scale Omics data and technology towards application in diseases/drug/treatment problem-based setting in the African and global context.
• Understand the broader approaches to quantitative studies that provide better support for work in biomedical science and mining various Omics data science.
• Apply statistical programming language such as R and R-studio in interpretation and mining Omics data science.
• Familiarize with best practices in Omics data science analyses and have a foundation in statistical inference.
• Apply and understand diverse aspects of quantitative work in biomedical science and Omics technologies will need in their professional lives.
• Appreciate the interplay between computational statistics and its application to Omics data visualization, to the approaches in clinical, Omics and public health research.

Associated Assessment Criteria for Exit Level Outcome 4

• Ability to write computer scripts and use High-Performance Computing for large-scale biomedical data.
• Appreciate the qualitative and quantitative methods in biomedical research and bio-industries principles, processes and all their facets.
• Familiarise with opportunities, standards and practice in a bio-industry sector related to biomedical research or career ambitions.
• Apply entrepreneurial skills and concepts; and with necessary competencies to run, involve or establish an entrepreneurial venture in a business context in the biomedical sector.
• Demonstrate an advanced level of communicative competence.
• Understand different types of databases and their applications.
• Grasp the key concepts of database systems and the database approach to information storage and manipulation.
• Design and implement biomedical database applications.
• Improve the performance of existing database applications and database curation.
• Understand the difficulties associated with multiple user access, including concurrent access, and assigning different user roles and levels of access.
• Design adequate backup, recovery, and security measures for a database installation, and understand the facilities provided by typical database systems to support these tasks.
• Understand the types of the task involved in database administration and the facilities provided in a typical database system to support these tasks.
• Maintain and develop web applications pertinent to biomedical and clinical data.

Associated Assessment Criteria for Exit Level Outcome 5

• Integrate different Omics and analyse biological functions using constraint-based models.
• Construct biological networks based on different Omics data, as well as integrative multi-omics networks, and perform topology analyses. Appreciate application of key machine learning methods for multi-omics analysis and biological function approaches pertinent to the association of various Omics-phenotypes.
• Identify key methods for analysis and integrating Omics data.

INTEGRATED ASSESSMENT

Assessment is based on the performance in coursework (45%) and the final examination (55%) scheduled at the end of each course. The final mark of each course is made up as follows: laboratory/computing techniques (15%); coursework mark (assignment pertinent to data mining, laboratory experiments, approaches description, computation tasks or research on theories/approaches that cover the course learning objectives) (30%) and final exam (55%), covering a range of theories and practices with respect to the goal assigned to the course.

A learner who fails with 45% - 49% may be granted a supplementary examination. A learner who achieves less than 45% will not qualify for the supplementary examination.

The proposed qualification will apply

• Formative assessment will be in a form of multiple-choice questions (MCQs) that will be applied at the beginning of each course. This assessment through MCQs will allow each proposed course, to (a) capture the level of the class to accordingly provide appropriate learner support and (b) enhance the learning process by giving allowing learners the opportunity to develop the valued knowledge, skills, and course concepts.
• Summative assessment will be applied in each proposed course to evaluate what the learner learned at the end (final exam) and/or during (through assignments and quizzes).

The qualification seeks to include the above two types of assessments overall proposed courses and provide timely feedback to learners to cater for a variety of learning outcomes.

The moderation process will include the following

• Verify that assessments are fair, valid, and consistent.
• Identify the need to re-design the assessment if required.
• Provide appeal procedures for dissatisfied learners.
• Provide a procedure for the re-assessment of learners.