Skip to navigation | Skip to main content | Skip to footer
Menu
Menu

Current postgraduate taught students

COMP60532: Introduction to BioHealth Informatics (2011-2012)

This is an archived syllabus from 2011-2012

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 P4 Lecture 2.15 Fri 09:00 - 17:00 -
Assessment Breakdown
Exam: 50%
Coursework: 50%
Lab: 0%

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

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
A1 A2 C4 D1 D2 D5 G1Understand the opportunities arising from integrating bio- and health informatics datasets.
  • Individual coursework
  • Presentation
  • Group coursework
A1 A2 A3 C4 D1 D2 D3 D5 G1Critically interpret functional genomics literature
  • Presentation
  • Individual coursework
  • Group coursework
A1 A2 A3 G1 G5Basic understanding of core concepts of human variation, pharmacogenomics and their role in personalised medicine
  • Individual coursework
  • Presentation
A1 B1 B3 C2 D1 D4 D5 G1 G5An understanding of clinical trial development and evaluation
  • Group coursework
  • Presentation
  • Individual coursework
A3 D4 D5 G5Understand the process of project grant application and review.
  • Presentation
  • Individual coursework
B1 B3 C4 D1 D4Basic machine learning and statistical strategies for biomarker discovery

Syllabus

Part 1:Functional genomics and translational medicine
- advanced bioinformatics
- medical data from hospitals
- hypothesis formulation and translational medicine
Part 2: Biomarkers and disease
- introduction to clinical trials
- biomarker discover from functional genomics data
- power anaylsis
- case studies
- applications of machine learning to medicine
Part 3: personalised medicine
- the life-cycle of a drug
- human variation and disease
- pharmacogenomics
- case studies