Skip to main content


CSC3432 : Biomedical Data Analytics and AI

  • Offered for Year: 2022/23
  • Module Leader(s): Dr Jaume Bacardit
  • Lecturer: Dr Paolo Zuliani
  • 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
Structured Guided LearningLecture materials301:0030:00Lectures non-synchronous online
Guided Independent StudyAssessment preparation and completion301:0030:00Lecture follow-up
Scheduled Learning And Teaching ActivitiesPractical112:0022:00Practicals, synchronous PIP sessions, if available. Otherwise additional synchronous online sessions
Guided Independent StudyProject work401:0040:00Coursework 1
Guided Independent StudyProject work401:0040:00Coursework 2
Scheduled Learning And Teaching ActivitiesDrop-in/surgery111:0011:00Synchronous PIP sessions, if avail. Otherwise additional synchronous online session. Ask Qs re lecs
Guided Independent StudyIndependent study271:0027:00Background 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

Other Assessment
Description Semester When Set Percentage Comment
Practical/lab report1M50max 2000 words on bioinformatics
Practical/lab report1M50max 2000 words on biomedical machine learning
Formative Assessments
Description Semester When Set Comment
Practical/lab report1ADraft report, max 500 words, on the design of a biomedical machine learning experiment
Assessment Rationale And Relationship

This module focuses on a very practical subject and hence an assessment based on coursework is the best option to evaluate the student’s knowledge. The coursework will assess the student’s ability to apply the module’s concepts in the a practical setting and will be assessed as practical reports, which is a suitable method for assessing the use of biological data analytics software.

Reading Lists