Graduate Diploma in 'Artificial intelligence' in genomics

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This course introduces students to the structure, function, and organization of cells, the basic units of life. Topics include organelles, membranes, cytoskeleton, and cellular communication. Students explore the cell cycle, apoptosis, and signaling pathways. Emphasis is placed on understanding how cellular processes relate to health and disease. This course lays the foundation for all subsequent genetics and molecular biology studies.

Students learn the structure and function of DNA and RNA, including replication, transcription, and translation. The course covers gene regulation and expression, with emphasis on cellular function and disease. Students explore practical applications in diagnostics and research. Case studies highlight how molecular genetics informs healthcare. This course builds essential knowledge for understanding genetic engineering and testing.

This course introduces techniques for modifying genes and genomes, including recombinant DNA technology, CRISPR, and cloning. Students learn applications in medicine, biotechnology, and research. Ethical considerations and laboratory safety are emphasized. Case studies show how genetic engineering supports therapeutic and diagnostic innovations. The course provides a foundation for modern genomic interventions.

Students study the human genome, including structure, organization, and functional elements. The course explores genetic variation and its impact on health and disease. Applications in personalized medicine, disease prediction, and research are highlighted. Students gain insight into genomics’ role in clinical decision-making. Practical examples link theory to real-world healthcare and biotech applications.

Students learn inheritance principles, Mendelian genetics, and trait transmission. The course covers genetic terminology, pedigree analysis, and common patterns of inheritance. Real-world examples connect genetics to human health and disease. Students develop foundational knowledge for clinical and research applications. This course prepares learners for advanced topics like mutations and testing.

This course examines types of genetic mutations and their effects on proteins and cellular processes. Students learn methods for mutation detection, interpretation, and analysis. Clinical and research case studies illustrate how mutations cause disease. Emphasis is placed on practical skills and critical thinking. The course prepares students for genetic testing and counseling roles.

Students explore the genetic basis of cancer, including oncogenes and tumor suppressor genes. The course covers tumor development, genomic instability, and predisposition syndromes. Diagnostic methods and molecular testing are introduced. Current advances in targeted therapy and precision medicine are highlighted. Students gain insight into genetics’ role in oncology and clinical care.

This course surveys common and rare genetic disorders. Students learn inheritance patterns, clinical manifestations, and diagnostic approaches. Case studies show how rare disorders inform treatment strategies. Emphasis is placed on real-world applications in healthcare. The course prepares students for entry-level roles in genetic support services.

Students learn molecular, cytogenetic, and biochemical methods of genetic testing. The course covers gene therapy principles, applications, and regulatory considerations. Case studies illustrate diagnostic and therapeutic use in clinical genetics. Ethical, legal, and patient-centered considerations are emphasized. Students develop skills applicable to labs, clinics, and biotech environments.

Students explore the foundational principles of genomics and artificial intelligence, with an emphasis on the structure, function, and analysis of genomic data. The course introduces DNA sequencing technologies, genomic file formats, and bioinformatics preprocessing methods. Students gain hands-on experience in applying machine learning techniques to genomic datasets, including supervised and unsupervised learning, feature selection, and dimensionality reduction. Deep learning architectures such as convolutional and recurrent neural networks are introduced in the context of biological sequence modeling. Emphasis is placed on developing computational skills and biological insight necessary for further applications in research, clinical, and biotech settings.

This course focuses on the core applications of AI in genomic research and diagnostics. Students learn to use machine learning and deep learning tools for gene prediction, genome annotation, variant calling, and disease risk modeling. Topics include genome-wide association studies (GWAS), functional annotation of genetic variants, and integration of multi-omic datasets. Real-world case studies illustrate how AI-driven workflows are transforming clinical genomics, population genetics, and precision medicine. Students work with publicly available genomic datasets and build end-to-end pipelines for variant interpretation and phenotype prediction. Ethical considerations related to genomic data use and AI-based decision-making are also addressed.

Students explore advanced applications of AI in personalized medicine, drug discovery, and single-cell genomics. The course covers AI methods for pharmacogenomics, patient stratification, and treatment outcome prediction. Students learn to analyze single-cell RNA sequencing data using machine learning for cellular clustering, lineage tracing, and expression profiling. AI-based approaches to drug target identification, molecular pathway modeling, and virtual screening are also discussed. Through guided projects, students apply concepts to clinical scenarios and therapeutic design. Emphasis is placed on translational relevance, data integration, and ethical challenges in deploying AI in healthcare and biotechnology industries.