This course introduces high-throughput sequencing technique, data structure and analysis pipeline, and also briefly explains the gene functional annotation, gene expression analysis, protein-protein interaction, drug sensitivity analysis, machine learning application, and disease risk model construction using high-throughput genomic data. Moreover, this lecture will also invite professionals to share their researches based on high-throughput genomic data.
This course covers a wide range of topics, including machine learning, deep learning, R programming language and data structures, DNA-seq and RNA-seq data structures, common gene databases, gene data analysis models, and practical examples. The aim is to provide students with an understanding of the fundamental principles and applications of machine learning, particularly in the practical context of medicine and bioinformatics.
This course introduces the analysis of high-throughput genomic data, including the establishment of a bioinformatics analysis environment, analysis pipelines (GATK best practices), the use of common genomic databases and tools, and practical gene expression analysis methods.