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DRPS : Course Catalogue : Deanery of Biomedical Sciences : Integrative Biomedical Sciences (Zhejiang)

Undergraduate Course: Introduction to Biomedical Informatics 1 (IBMS08011)

Course Outline
SchoolDeanery of Biomedical Sciences CollegeCollege of Medicine and Veterinary Medicine
Credit level (Normal year taken)SCQF Level 8 (Year 1 Undergraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryIntroduction to Biomedical Informatics 1 aims to provide students with an overview of the fundamental technological concepts of Biomedical Informatics using real-world applications as examples. The course will also introduce the various technological sub-fields of Biomedical Informatics. This includes genomics, working with online medical resources, public health informatics, simulation and modelling, and artificial intelligence. This is intended to give students a broad idea of the field and what it entails, with an opportunity to go into more depth in future years.
Course description Introduction to Biomedical Informatics 1 aims to give students foundational skills in computing and programming, as well as broad overview of the technological fields comprising biomedical informatics and show how they are applied to real-life biomedical problems.

You will learn about operating systems, versioning and programming, as well as about biomedical information retrieval, sequence analysis, natural language processing, probabilistic reasoning, modelling and simulation.

Using these technologies in practical classes and assignment will allow you to practice and reinforce computing skills and to use them to address questions from the biomedical field. You will also improve your academic skills, including versioning and documenting your work, literature research, completing a group project, presenting, giving and receiving feedback and reflective practice.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2018/19, Not available to visiting students (SS1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 28, Seminar/Tutorial Hours 14, Supervised Practical/Workshop/Studio Hours 56, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 98 )
Assessment (Further Info) Written Exam 30 %, Coursework 70 %, Practical Exam 0 %
Additional Information (Assessment) Exam: 30% total course mark«br /»
Coursework: 70% total course mark«br /»
Including: practical work assessment portfolio (40%), data analysis project and presentation (30%)
Feedback Students will receive summative feedback on their portfolio and group presentation.

Both in-course assessments will be preceded by specific formative feedback: For the portfolio, students will receive feedback on their portfolio so far mid-semester, and will be given a chance to change their practice, and also to amend earlier entries. Hosting the portfolios on github will make it easy to assess whether students have made changes following formative feedback, and this will be a marking criterion in the summative assessment.

For the group presentation, groups will have a meeting with a faculty member several weeks before the assignment is due to present their preliminary ideas and receive formative feedback. This will help correct misconceptions, and also gives groups an intermediate milestone in their project timeline.

Additional formative feedback will be given throughout the course, especially in practical and tutorial sessions, so students can check their understanding as they progress. This includes opportunities for formative peer feedback. Students are also encouraged to add a reflective element to their practical write-ups.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Understand basic concepts of computer programming and the differences between operating systems
  2. Develop technical skills, including simple programming tasks in Python; the use of UNIX systems, the use of version control tools and the use of web authoring tools to build websites and simple online user interfaces, making these skills an integral part of their professional practice
  3. Describe and understand the technological sub-disciplines comprising Biomedical Informatics; including information retrieval, sequence analysis, natural language processing, probabilistic reasoning, modelling and simulation
  4. Know about available online medical informatics tools, resources and search engines
  5. Apply their software skills to real-life biomedical data; and plan and execute a biomedical informatics project
Reading List
Biomedical informatics and Functional Genomics. Jonathan Pevsner. Wiley-Blackwell
Additional Information
Graduate Attributes and Skills This course follows the core areas of technological knowledge for Biomedical Informatics, as identified by the American Medical Informatics Association (Kulikowski et al., J Am Med Inform Assoc 19, 2012): Information documentation, storage, and retrieval, Natural language processing, Semantic technologies, Representation of logical and probabilistic knowledge and reasoning, Simulation and modeling, Software engineering.
In addition, the course develops the following core graduate skills and attributes for graduates in Bioinformatics, as identified by Welch et al. (2014) PLoS Comp Biol 10(3).
programming, software engineering, system administration, scripting languages, open source software repositories, web authoring tools, web-based user interface implementation technologies, version control tools
analysis of biological data; retrieving and manipulating data from public repositories; ability to manage, interpret, and analyze large data sets; broad knowledge of bioinformatics analysis methodologies; familiarity with functional genetic and genomic data; expertise in common bioinformatics software packages, tools, and algorithms
Statistics and Mathematics:
application of statistics in the contexts of molecular biology and genomics, mastery of relevant statistical and mathematical modeling methods
genomics, systems biology, next generation sequencing
time management, project management, curiosity, self-motivation, ability to synthesize information, ability to complete projects, critical thinking, dedication, ability to communicate scientific concepts, analytical reasoning, scientific creativity, collaborative ability
Keywordsbiomedical informatics,programming,online bioinformatics tools
Course organiserDr Melanie Stefan
Tel: (0131 6)51 1711
Course secretaryMs Cristina Matthews
Tel: (0131 6)51 1346
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