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Comprehensive cancer analysis workshop covering genomics, gene expression, pathways, and multi-omic integration. Hands-on training in bioinformatics tools and techniques for cancer research and clinical data interpretation.
Learn R programming essentials: scripting, statistical tests, loops, functions, linear models, and R Markdown in this comprehensive workshop for beginners.
Comprehensive workshop on pathway and network analysis techniques for gene lists, covering visualization, over-representation, regulatory networks, and gene function prediction using various tools.
Comprehensive workshop on machine learning in bioinformatics, covering decision trees, neural networks, hidden Markov models, and practical applications using Keras and Scikit-Learn.
Comprehensive exploration of cancer genomics, covering databases, sequencing analysis, gene expression, mutations, and cloud-based bioinformatics tools for reproducible research and big data integration.
Learn the fundamentals of R programming for data analysis and visualization through hands-on sessions covering key concepts and practical applications.
Comprehensive exploration of high-throughput biology, covering sequencing, RNA analysis, and network pathways with hands-on tutorials using popular bioinformatics tools.
Explore microbiome research through bioinformatics and big data analytics, covering topics from metagenomics to machine learning for biological data and microbiome database mining.
Learn metabolomics data analysis, including technologies, statistics, pathway analysis, and database exploration. Gain practical skills through tutorials and lectures on this emerging field.
Explore key bioinformatics concepts and tools for analyzing cancer genomic data, including mutation detection, gene expression, and data integration techniques.
Explore cancer genomics through mapping, gene fusion, copy number alterations, somatic mutations, gene expression profiling, and network analysis. Gain insights into cutting-edge bioinformatics techniques for understanding cancer at the molecular level.
Explore cancer genomics data analysis using ICGC Data Portal and learn pathway and network analysis techniques for understanding cancer biology.
Explore high-throughput sequencing tools, variant calling, visual analysis, Galaxy integration, and de novo assembly techniques for advanced bioinformatics applications.
Explore cancer genomics through data visualization, alignment, mutations, pathway analysis, and gene expression profiling. Learn to interpret biological data for cancer research.
Learn to analyze flow cytometry data using R, covering exploration, processing, quality assurance, static gating, and advanced clustering techniques for comprehensive data interpretation.
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