In this module, you will explore the fundamentals of molecular biology, focusing on the structure and function of nucleic acids, proteins, and other essential biomolecules. You will learn how DNA and RNA store, replicate, and express genetic information. We will cover transcription and translation, revealing how proteins are synthesised and function within the cell. Additionally, you will examine gene regulation, mutations, and the molecular basis of genetic variation and evolution. Understanding these principles is essential for analyzing and interpreting biological data using bioinformatics tools.

In this module, you will explore crucial molecular biology concepts vital for bioinformatics. Create a comprehensive concept map to understand DNA replication and gene expression processes. Study DNA sequencing principles to learn methods for decoding genetic information, and examine gene structure and regulation in eukaryotes and prokaryotes. Discover the central dogma of molecular biology, describing the flow of genetic information from DNA to RNA to protein. This module builds a solid foundation for applying computational tools in bioinformatics, enhancing your knowledge and skills in this fascinating field.

This module teaches you how to leverage RNA sequencing data for patient subtyping. You will master the entire workflow, from raw data acquisition to grouping samples. Start with hands-on experience in extracting and normalizing RNA-seq data from the NCBI Gene Expression Omnibus (GEO) database. Then, explore and apply two clustering approaches: Hierarchical Clustering and the Louvain Algorithm, to identify meaningful patient subtypes. Conclude by comparing the effectiveness of these clustering methods and learning survival analysis using Kaplan-Meier curves.

In this module, you will explore machine learning applications for cell type classification using single-cell RNA sequencing (scRNA-seq) data. Learn the full workflow, from data acquisition and preprocessing to feature selection and classification algorithm implementation. Engage in hands-on exercises to build and evaluate models for accurate cell type identification, gaining practical insights into scRNA-seq data analysis for biological research.

Explore gene-gene associations using methylation and mRNA data from The Cancer Genome Atlas (TCGA). Learn to process and analyze high-dimensional omics data, construct gene association networks, and handle real cancer datasets. Master normalization techniques, network construction methods, and visualizations to enhance your biological understanding through practical, hands-on experience.

In this module, you will explore the core concepts and practical applications of gene enrichment and pathway analysis in biological research. Learn to analyse gene lists, understand Gene Ontology structures, and interpret biological pathways. Gain hands-on experience with industry-standard tools like DAVID and STRING to transform complex genomic data into meaningful insights. Emphasise understanding pathway networks and disease associations to prepare for real-world genomics research applications.

Explore Natural Language Processing (NLP) with a focus on biomedical applications. Start with core NLP concepts and progress through essential libraries and preprocessing techniques for medical text data. Delve into specialised topics like Named Entity Recognition and pattern matching in clinical contexts. Learn about transformer architectures and their applications in biomedical text analysis. Gain hands-on experience with tools like BioBERT and NLTK to process, analyse, and extract insights from medical literature.

Explore medical text mining and knowledge extraction in this module. Begin by examining the unique characteristics of medical text and PubMed data organization. Progress through medical ontologies and specialised language models like BioBERT for a solid text analysis foundation. Finally, extract and analyze complex medical relationships, including disease-symptom associations, drug interactions, and comorbidity patterns. Apply advanced NLP techniques to gain actionable insights from medical literature.

In this module, you'll learn the essential knowledge and techniques for working with raw DNA data, including understanding its structure and organization, like SNP data. You'll dive into genetic distance metrics to identify genetic relationships between individuals and explore common distance calculation algorithms and DNA matching techniques. You'll also learn methods for statistically analyzing genetic match results and building a relationship prediction system. Finally, you'll explore visualization and network analysis approaches to gain deeper insights from DNA match data, create interactive chromosome-level visualizations, and use graph-theoretic methods to uncover complex familial relationships within the DNA match network.