The MBC information booklet - RMIT University

CHAPTER 4 Bioinformatics
4.1 Introduction
4.2 Courses
4.2.1 COSC2308—
Advanced
Topics in
Bioinformatics
Chapter 4: Bioinformatics
As the rate of accumulation of biological data increases exponentially, computational tools
for the storage, annotation, querying and modelling of this data become paramount, and
can impose a limitation on data analysis. The courses in the Bioinformatics cluster cover
both the creation of algorithms for the analysis of biological data and the use of these to
derive useful and novel information. Computational Biology describes the limitations of
current algorithms, and how we can develop superior data analysis and querying tools.
Bioinformatics 1 and 2 describe the use of these tools in the analysis and querying of data,
from a biologist's perspective. Bioinformatics 2 in particular deals with the statistical basis
underlying the tools. Advanced Topics is a course designed to introduce selected current
problems in biological data analysis, and how these may be solved by computational
techniques.
Pursuant to demand, the following courses are available for students who wish to choose
this cluster. The courses are:
• COSC2308—Advanced Topics in Bioinformatics (see page 21)
• MATH1300—Analysis of Medical Data (see page 22)
• BIOL2034— Bioinformatics (see page 22)
• COSC2151—Introduction to Computational Biology (see page 22)
Aim
This course presents state-of-the-art algorithms for efficient data analysis and advanced
applications. Students will acquire knowledge about advanced algorithms, architectures
and data structures, learn how to choose appropriate ones to solve complex problems,
and be able to explain their decisions. This course builds on the capabilities acquired in
Computing Fundamentals, and complements the material of the course BIOL2034
Bioinformatics [1].
Objectives
Upon successful completion of this course students should be able to:
• Select appropriate heuristics to address NP-hard problems
• Choose appropriate algorithms and data structures to solve a complex problem
• Compare and evaluate strengths and weaknesses of data structures and algorithms,
and communicate this understanding
Prerequisites
• BIOL2034 Bioinformatics 1
Syllabus
• Methods related to intelligent systems such as simulated annealing, neural networks
and genetic algorithms
• Heuristics for sequence analysis, such as Hidden Markov Models and dynamic
programming
• The principles underlying a range of advanced algorithms and data structures,
including most of the following: suffix ties, suffix trees, depth first search, breadth first,
density computation.
• How to compare and evaluate strengths and weaknesses of data structures and
algorithms
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