The "Omics for Non-Biologists" course is specifically designed to provide AI students with the necessary understanding of essential and widely used Omics techniques, with a special emphasis on the application of R programming language. The course aims to prepare students for Omics-based AI projects and expose them to the common tools used to process Omics data, while also equipping them with a beginner-level understanding of R. Prior knowledge about R will be helpful for the students but nevertheless the course begins with a crash course on R programming for beginners, enabling students to grasp the fundamentals of the language and its applications in Omics data analysis. This introductory session ensures that all students have a solid foundation in R before diving into the core topics of the course.
Throughout the course, students will delve into six Omics techniques: Genomics, Transcriptomics, Proteomics, Metabolomics, Epigenomics and Metagenomics. Each technique is explored in both theoretical and practical sessions. The theoretical sessions provide an overview of the underlying principles and data generation processes, emphasizing the significance of these techniques in the context of AI. The practical sessions focus on hands-on skills acquisition, where students gain experience using R and other computational tools to process Omics data. The emphasis is on generating important figures and enabling students to interpret them effectively. These practical sessions follow a scenario-based approach centered around bladder cancer, allowing students to engage in team-based activities. Through collaborative exploration and integration of findings from different Omics approaches, students develop a holistic understanding of how Omics data can be utilized in AI applications. To reinforce their understanding of the course materials and enhance problem-solving skills, students are assigned homework after each session. These assignments present real-world challenges that require the application of AI techniques in conjunction with R programming. By tackling these challenges, students not only gain practical experience but also solidify their comprehension of the course content.