Master of Science in Computational Health Informatics

Purpose

The primary purpose of the Master of Science in Computational Health Informatics is to provide advanced and specialised training in biomedical data science and technology through:

• Integrative approaches focused on current rapidly growing various "Omics" challenges in the African and global context.
• "Omics" techniques in the context of biomedical research, pharmaceutical and biotechnology industries.
• A specialised understanding of the ethical, legal, and social issues associated with these advances

to prepare graduates for a range of bio-industry careers in biomedical science.

"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 a set of metabolites present within an organism, cell, or tissue).

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

• Demonstrate advanced ability to write, design and implement computer scripts and advance use of High-Performance Computing and Biomedical databases for big biomedical data.
• Demonstrate advanced ability to formulate and test a hypothesis for biomedical data science to propose solutions to complex problems and an insight into their proposed solutions.
• Demonstrate advanced and specialised skills in mapping diseases, Omics-phenotype/drug response association studies, population Omics structure and advanced ability in turning biomedical data into information relevant for clinical applications.
• Demonstrate proficiency in analytics pipeline and methodologies of large-scale Omics data science and technology towards application in diseases/drug/treatment problem-based setting in Africa and Global context.
• Demonstrate knowledge and understand the principles of the management and process of large-scale biomedical data science.
• 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.
• 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 (EHR), biomedical imaging, and "Omics", provide an opportunity to improve understanding of the mechanisms of disease and ultimately to improve human health care.

The Omics 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 "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 visualising big macromolecular data using novel computational approaches, machine intelligence and deep learning methods. Through High-Performance Computing (HPC), 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 responses 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 prioritising in silico mutations leading to clinical diagnostics and personalised medical treatment of patients on a much broader scale than ever before possible with older methods.

The exposure that current, and recent trainees and postgraduate learners receive in big biomedical data science remains informal and inconsistent. In addition, given that the field is new and highly multi-disciplinary, current honours, post-honours programs and data science qualifications do not offer computational paradigms in mining large-scale biomedical data science from various Omics technology platforms or prepare postgraduates for competitive entry into a range of biomedical industry or academic careers.

There is a need to introduce learners with an advanced and specialised background in particular disciplines to the development and use of "Omics" tools for research, to key strategic considerations in the fundamental concepts of machine learning and computational paradigms of large-scale "Omics" data; and their applications to the local and African continent context and in addressing the health challenges of our society.

The qualification will focus on contextually relevant local biomedical and large-scale "Omics" data science challenges, influencing the health of South Africans and Africans.

The qualification will strengthen the pipeline of postgraduate learners and researchers in the biomedical sciences. The challenges of promoting career entry and closing identified skills gaps in the bioscience/biotechnology, healthcare and accelerating therapeutics sectors persist. Providing a qualification that trains learners with advanced and specialised skills that could transfer into a range of bio-industry careers will fill the gap and position for careers in biomedical science.

Recognition of Prior Learning (RPL)

The institution has an approved Recognition of Prior Learning (RPL) policy which is applicable 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 Honours in Bioinformatics, NQF Level 8.

Or

• Bachelor of Science Honours in Bioinformatics and Computational Biology, NQF Level 8.

Or

• Bachelor of Science Honours in Computer Science, NQF Level 8.

Or

• Postgraduate Diploma in Computer Science, NQF Level 8.

Or

• A relevant qualification in the related field, NQF Level 8.

This qualification consists of the following compulsory and elective modules at National Qualifications Framework Level 9 totalling 180 Credits.

Compulsory Nodules, Level 9, 120 Credits

• Bio-Computing, 20 Credits.
• Bioinformatics programming with python, 20 Credits.
• Machine learning and biomedical data science, 20 Credits.
• Research project, 60 Credits.

Elective Modules, Level 9, 60 Credits (Select four modules)

• Epigenetics, Gene Expression and Proteomics Data Mining, 15 Credits.
• Bioinformatics for Next Generation Sequencing Technologies, 15 Credits.
• Computational Population Omics Structure, 15 Credits.
• Omics-Wide Association Studies, 15 Credits.
• Knowledge-based Protein-Protein Network, 15 Credits.
• Data science for Epidemiology and Health Informatics, 15 Credits.
• Pharmacomicrobiomics, 15 Credits.
• Computational Forensic Omics, 15 Credits.
• Computational Phylogenetics, 15 Credits.

1. Demonstrate an advanced ability to write, design and implement computer scripts and advance use of High-Performance Computing and Biomedical databases for big biomedical data.
2. Demonstrate an advanced ability to formulate and test a hypothesis for biomedical data science to propose solutions to complex problems and an insight into their proposed solutions.
3. Demonstrate advanced and specialised skills in mapping diseases, Omics-phenotype/drug response association studies, population Omics structure and advanced ability in turning biomedical data into information relevant for clinical applications.
4. Demonstrate proficiency in analytics pipeline and methodologies of large-scale Omics data science and technology towards application in diseases/drug/treatment problem-based setting in Africa and Global context.
5. Demonstrate knowledge and understand the principles of the management and process of large-scale biomedical data science.
6. 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.
7. 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.
8. Demonstrate the ability to formulate and test a hypothesis for digital biomedical data science to propose solutions to problems using quantitative approaches.
9. Demonstrate the ability to write computer scripts and use High-Performance Computing for large-scale biomedical data.
10. Demonstrate entrepreneurial skills and concepts, and with necessary competencies to run, involve or establish an entrepreneurial venture in a business context in the biomedical sector and demonstrate an appropriate level of communicative competence.
11. Demonstrate proficiency in fundamental analytics pipelines and methodologies in large-scale Omics data and technology towards application in diseases, drugs and treatment problem-based settings in the African and global context.

Associated Assessment Criteria for Exit Level Outcome 1

• Apply advanced knowledge and the latest emerging technologies in accessing the clusters, biomedical databases, and git.
• Apply advanced Linux programming and High-Performance Computing skills in processing, storing, and transferring big Omics data.
• Apply advanced skills in partitioning problems for parallel computing for big biomedical data science, biomedical databases curation, management, and design.
• Apply advanced skills for productive computational biomedical.
• Apply advanced use of Python features for biomedical problem-solving.
• Design and develop Python computer programmes to mine and model big biomedical data and underlying Omics approaches.
• Implement the Python programming on biostatistical methods for high-dimensional biomedical data.

Associated Assessment Criteria for Exit Level Outcome 2

• Use advanced statistical programming language, R and R-studio for the interpretation and mining of Omics data science.
• Apply advanced computation biostatistics in biomedical concepts to solve problems of various Omics and related downstream Omics data analysis.
• Apply cutting-edge techniques and best practices to report, interpret, store, manage and analyse data.
• Use machine learning within biostatistics to solve some problems of biomedical and related down programme data analysis.

Associated Assessment Criteria for Exit Level Outcome 3

• Understand the principles underlying each Omics-phenotype association study.
• Choose appropriate tools for Omics-phenotype association studies, perform analysis and interpret the underlying results
• Discuss limitations of current Omics association approaches in the field of complex diseases
• Shape the next generation of Omics association studies by appreciating new disease-mapping and other Omics-Wide Association Studies (OWAS) such as microbiomics, and transcriptomics techniques to overcome current limitations.
• Browse, search, submit and retrieve proteomics data from widely used public repositories and databases.
• Apply the new insight and role of epigenomics data analysis and its critical role in gene expression and disease-mapping.
• Apply bioinformatics approaches and standard pipelines in proteomics and gene expressions bioinformatics data analysis and recognise their importance in biomarker/drug discovery and disease mapping.

Associated Assessment Criteria for Exit Level Outcome 4

• Appreciate different pharmacogenomic, microbiomics and integrative Pharmacomicrobiomics approaches to understand drug responses and treatments variability.
• Describe principles of Pharmacogenomics and Pharmacomicrobiomics.
• Use, decode and interpret different Pharmacogenomics and Pharmacomicrobiomics databases. Use statistical data analysis for Pharmacomicrobiomics and Microbiome Genome-wide Association Studies.
• Apply machine intelligence approaches and statistical methods for evaluating forensic evidence.
• Apply the knowledge and skills needed to analyse and interpret deoxyribonucleic acid (DNA) evidence for legal proceedings, like criminal trials and paternity determinations.
• Describe and differentiate between different techniques used for DNA profiling.
• Explain how different DNA profiles can be applied in DNA forensics.

Associated Assessment Criteria for Exit Level Outcome 5

• Understand various models of sequence evolution, and different computational and inference methods for phylogenetic analysis from editing raw epidemiological sequence data.
• Appreciate working with public sequence repositories (BLAST, uploading data) and perform phylogenetic analyses.
• Describe whole systems of biological components with the aid of integrative computational approaches.
• Analyse the structure and understand dynamics of the system, both quantitative and qualitative, and build predictive models using post-Omics summary statistics.
• Map high-throughput biological datasets to functional knowledge.
• Perform post-analysis of results from high-throughput computational approaches.

Associated Assessment Criteria for Exit Level Outcome 6

• Understand basic epidemiological models of disease transmission dynamics and be able to analyse the phenomena.
• Interpret and compute appropriate epidemiological measures in order to describe disease frequency, association and attributable risk for given scenarios.
• Measure sensitivity, specificity, and positive and negative predictive values, in order to interpret these values in the context of screening.
• Describe and elucidate different types of confounding that may occur in epidemiological studies and be able to deal with the biases.
• Design a retrospective or prospective clinical study, including cohort, case-control or cross-sectional study.
• Analyse different epidemiological study designs and describe their strengths and weaknesses.

Associated Assessment Criteria for Exit Level Outcome 7

• Understand various population-genetic phenomena and how it influences the properties of genetic variation and non-genetic variation.
• Gain an understanding of the statistical methods used for the analysis of population-Omics data.
• Appreciate various clustering-based approaches to understand population-Omics patterns and underlying genetics relatedness.
• Receive rigorous training in the interplay of statistical genetics and method for population Omics.

Associated Assessment Criteria for Exit Level Outcome 8

• Reflect on theories and application in biomedical science.
• Deal with complex issues systematically and creatively, to design and critically appraise research in Omics large-scale data, to make a sound judgement using the biomedical data, Omics approaches or multi-omics integrative approaches and information at their disposal,
• Apply appropriate conclusions clearly to specialist and non-specialist audiences.

Associated Assessment Criteria for Exit Level Outcome 9

• Understand broader approaches to quantitative studies that provide better support for work in biomedical science and mining various Omics data science.
• Use statistical programming language such as R and R-studio in interpretation and mining Omics data science.
• Familiarise with best practices in Omics data science analyses and have a foundation in statistical inference.
• Use and understand diverse aspects of quantitative work in biomedical science and Omics technologies will need in their professional lives.
• Describe the interplay between computational statistics and its application to Omics data visualisation, to the approaches in clinical, Omics and public health research.

Associated Assessment Criteria for Exit Level Outcome 10

• Familiarise with opportunities, standards and practice in a bio-industry sector related to biomedical research or career ambitions.
• Use entrepreneurial skills and concepts; and with necessary competencies to run, involve or establish an entrepreneurial venture in a business context in the biomedical sector.
• Apply an advanced level of communicative competence.

Associated Assessment Criteria for Exit Level Outcome 11

• 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 tasks 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.
• Integrate different Omics and analysis of biological functions using constraint-based models.
• Build biological networks based on different Omics data, as well as integrative multi-omics networks, and perform topology analyses.
• Apply 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 will be ongoing, whether the learner is engaged in course work or research. Assessment will evaluate a learner's ability to grasp a problem, work creatively with constraints, gather, analyse, synthesise, evaluate, and interpret the necessary information, and formulate, articulate, develop and represent a relevant and sophisticated response.

Assessment is based on performance in coursework and exams in the first year and a dissertation in the second year. To pass the academic year, the learner must obtain an overall average of at least 50% with sub-minima of the research project and 50% for the combined coursework.

The evaluation of each module is based on the performance in coursework (formative) and the final examination (summative) scheduled at the end of the qualification.

Formative assessment focuses on the learner's ability to generate and develop ideas and possible solutions within the requirements and constraints posed by the problem under investigation. Formative assessment is viewed as a response to the learner's output with the possibility of further development upon critical reflection.

Summative assessment focuses on the learner's response to the requirements and constraints posed by the problem and will take the form of a final examination and oral presentation.

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