AI for Life Science and Bioinformatics
About This Course
This course equips participants with practical skills to apply artificial intelligence and computational methods to life science and bioinformatics data. Learners will explore how biological information can be collected, prepared, analysed, and interpreted to support research in genomics, transcriptomics, proteomics, drug discovery, and personalised healthcare.
Participants will learn the foundations of bioinformatics, including biological databases, sequence analysis, alignment, gene expression, genomic variation, and protein structure analysis. They will work with complex biological datasets to identify patterns, compare sequences, detect variants, and investigate relationships between genes, proteins, biological functions, and disease-related processes.
The course also introduces machine learning techniques for classifying biological data, predicting outcomes, detecting anomalies, clustering samples, and identifying important features. Learners will apply suitable evaluation methods to assess model performance and avoid issues such as overfitting, biased data, and incorrect biological interpretations.
Through hands-on activities, participants will use AI-assisted analytical workflows to explore datasets, visualise results, summarise scientific findings, and communicate evidence-based insights. Emphasis is placed on data quality, reproducibility, privacy, ethical use, model transparency, and the validation of AI-generated conclusions by qualified professionals.
By the end of the course, learners will be able to develop bioinformatics analysis workflows, apply AI techniques to life science data, evaluate analytical results, and present meaningful findings that support biological research and informed scientific decision-making.
What You'll Learn
LO1: Recognize significant trends and aberrant results in bioinformatics data using statistical techniques
LO2: Use statistical tests and data analytics tools to estimate uncertainties and determine data acceptability.
LO3: Review datasets to uncover trends or patterns and identify potential causes of unacceptable data.
LO4: Develop new methods for analyzing large, complex bioinformatics datasets using specialized modeling software.
LO5: Facilitate discussions on applying big data analytics to examine bioinformatics issues and derive insights.
Learning Outcome:
Topic 1: Bioinformatics Fundamentals and Biological Sequence Analysis
Topic 2: Protein Structure Analysis and Genomic Variant Detection
Topic 3: Transcriptomics, Genomics and Gene Expression Analysis
Topic 4: Machine Learning for Biological Pattern Discovery and Prediction
Topic 5: Bioinformatics Data Visualisation and Scientific Insight Communication
Entry Requirements
Knowledge and Skills
• Able to operate using computer functions
• Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
Attitude
• Positive Learning Attitude
• Enthusiastic Learner
Experience
• Minimum of 1 year of working experience
Target Age Group: 21-65 years old