ARTIFICIAL INTELLIGENCE IN BIOINFORMATICS

CSE5BIO

2020

Credit points: 15

Subject outline

This subject introduces artificial intelligence (AI) in bioinformatics and biomedicine. It covers complex biological analysis, the application of AI principles, and the integration of diverse biological content through different AI technologies. On completing this subject you will be able to understand and apply AI techniques such as data modelling, machine learning, deep learning, statistical methods and data mining in Bioinformatics and Biomedicines problems. They will also understand how AI in bioinformatics technologies can be applied to DNA,RNA and protein structure and folding problems, molecular interactions, drug discovery, digital health, genetics and metabolic pathways.

SchoolSchool Engineering&Mathematical Sciences

Credit points15

Subject Co-ordinatorRajalakshmi Rajasekaran

Available to Study Abroad StudentsYes

Subject year levelYear Level 5 - Masters

Exchange StudentsYes

Subject particulars

Subject rules

PrerequisitesN/A

Co-requisitesN/A

Incompatible subjectsN/A

Equivalent subjectsN/A

Special conditionsN/A

Graduate capabilities & intended learning outcomes

01. Analyse the characteristics of biological and biomedical data using AI approaches.

Activities:
In lectures and tutorials students learn different types of biological datasets using AI approaches such as machine learning and data mining.

02. Select and effectively use existing AI tools to solve bioinformatics tasks such as pattern recognition and gene identification.

Activities:
In lecture and tutorials students will choose most appropriate tools to given problems. Students will also learn how to use the tool to process various biological data.

03. Evaluate and interpret the results generated by bioinformatics tools to a high standard.

Activities:
In lecture and tutorials, students will evaluate and interpret the results generated by bioinformatics tools.

04. Clearly present data analysis results of molecular interactions and biological knowledge discovery.

Activities:
In lectures and tutorials, students will learn the most effective way to present the data generated by bioinformatics tools

Subject options

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Start date between: and    Key dates

Melbourne, 2020, Semester 1, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorRajalakshmi Rajasekaran

Class requirements

Laboratory Class Week: 11 - 22
One 2.0 hours laboratory class per week on weekdays during the day from week 11 to week 22 and delivered via face-to-face.

Lecture Week: 10 - 22
One 2.0 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Assessments

Assessment elementComments% ILO*
One 3-hour examinationHurdle Requirement: To pass the subject, a pass in the examination is mandatory.50 02, 03, 04
Laboratory classes exercise equivalent to 100 words per laboratory class (total 1000 words)10 02
First assignment equivalent to 1200 wordsA written report on biological and biomedical data and their related issues using AI approaches20 01, 02, 03, 04
Second assignment equivalent to 1200 wordsA written report on various AI approaches in emerging topics such as molecular interactions and biological knowledge discovery.20 01, 02, 03, 04