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CSC3432 : Biomedical Data Analytics (Inactive)

  • Inactive for Year: 2019/20
  • Module Leader(s): Dr Jaume Bacardit
  • Lecturer: Professor Natalio Krasnogor
  • Owning School: Computing
  • Teaching Location: Newcastle City Campus
Semester 1 Credit Value: 20
ECTS Credits: 10.0


1.       To familiarise students with the fundamental computational approaches used for tackling biological and biomedical data handling and analysis
2.       To introduce the concepts of algorithm design for biological/biomedical data
3.       To develop skills in algorithm design with an emphasis on solving biological/biomedical problems
4.       To understand the most appropriate type of algorithms for differing analytical problems in biology and biomedicine and to introduce some of the most appropriate implementation strategies.

Outline Of Syllabus

1.       The broad spectrum of data types in biology and biomedicine
2.       Fundamental computational algorithms for the analysis of biological/biomedical data
3.       Basic biological/biomedical algorithm design
4.       Algorithms for sequence assembly and annotation
5.       Sequence alignment
6.       Protein structure prediction
7.       Analysis of high-throughput biological data
8.       Biological network construction

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Guided Independent StudyAssessment preparation and completion400:3020:00Revision for end of semester exam and exam duration
Guided Independent StudyAssessment preparation and completion361:0036:00Lecture follow-up
Scheduled Learning And Teaching ActivitiesLecture361:0036:00Lectures
Scheduled Learning And Teaching ActivitiesPractical122:0024:00Practicals
Guided Independent StudyProject work121:3018:00Coursework
Guided Independent StudyIndependent study661:0066:00Background reading
Teaching Rationale And Relationship

Lectures will be used to introduce the learning material and for demonstrating the key concepts by example. Students are expected to follow-up lectures within a few days by re-reading and annotating lecture notes to aid deep learning.

This is a very practical subject, and it is important that the learning materials are supported by hands-on opportunities provided by practical classes. Students are expected to spend time on coursework outside timetabled practical classes.

Students aiming for 1st class marks are expected to widen their knowledge beyond the content of lecture notes through background reading.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Description Length Semester When Set Percentage Comment
Written Examination901A40N/A
Other Assessment
Description Semester When Set Percentage Comment
Practical/lab report1M30max 2000 words
Practical/lab report1M30max 2000 words
Assessment Rationale And Relationship

he examination will assess the student’s knowledge of the fundamental theories of genome and post genomic data analysis and is an appropriate technique for assessing this work.

The practical component will assess the students ability to apply theory in the a practical setting and will be assessed as practical reports, which is a suitable methods for assessing the use of biological data analytics software.

NB. This module has both “Exam assessment” and “Other assessment” (e.g. coursework). If the total mark for either falls below 35%, the maximum mark returned for the module will normally be 35%

Reading Lists