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Current postgraduate taught students

COMP60532: Introduction to BioHealth Informatics (2010-2011)

This is an archived syllabus from 2010-2011

Introduction to BioHealth Informatics
Level: 6
Credit rating: 15
Pre-requisites: No Pre-requisites
Co-requisites: No Co-requisites
Lecturers: Andrew Brass
Course lecturer: Andrew Brass

Additional staff: view all staff
Timetable
SemesterEventLocationDayTimeGroup
Sem 2 P3 Lecture 2.15 Tue 09:00 - 17:00 -
Assessment Breakdown
Exam: 50%
Coursework: 50%
Lab: 0%

Themes to which this unit belongs
  • Health Informatics
  • Computational Biology

Introduction

BioHealth Informatics is a rapidly developing multi-disciplinary field that combines biological and genetic information with clinical data and computer information systems. It has been driven by the realisation that bio-informatics and health informatics must achieve a merger of standards, computer systems and data representations if progress is to continue towards the vision of post-genomic medicine.

Aims

The aim of this module is to introduce students to significant biohealth informatics themes and principles grounded by reference to high profile existing biohealth informatics projects and activities.

Programme outcomeUnit learning outcomesAssessment
G5Understand the opportunities arising from integrating bio- and health informatics datasets.
  • Individual coursework
  • Presentation
G5Understand the barriers to interoperable bioinformatics and health informatics datasets.
  • Individual coursework
  • Presentation
G5Handle and analyse bioinformatics and health informatics datasets.
  • Individual coursework
  • Presentation
G5Cooperate and communicate ideas in a collaborative environment.
  • Presentation
  • Individual coursework
G5Understand the process of project grant application and review.
  • Individual coursework
  • Presentation

Syllabus

Using a number of high-profile biohealth informatics projects as examples the following areas will be identified and explored:

Opportunities in the postgenomic world (phenotype and genotype). Examples include:
Drug Discovery; Treatment Optimisation; Cross-species hypothesis generation Epidemiology and the UK Biobank

Advanced Bioinformatics ? SNPs, transcriptomics, proteomics, metabolomics, epigenetics
Data acquisition - Metadata & Provenance, Data Quality (myGrid)
Data storage and access - web services, security, Grid computing, the Semantic Web
in silico experimental workflows - documentation, repeatability and workflow management
Applications - usability, visualization, dissemination, communication of results
Sociology of Collaboration ? cultural differences of biology, computer science and medicine (evidence, safety, ownership, governance, regulation)
Hazards ? ethics of genetic pedigrees; information overload and information underclasses