Workshops

This 5-day workshop will cover the key bioinformatics concepts and tools required to analyze cancer genomic data sets and access and work with data sets in the Cloud. Participants will gain practical experience and skills to: Visualize genomic data; Analyze cancer –omic data for gene expression, genome rearrangement, somatic mutations, and copy number variation; Analyze and conduct pathway analysis on the resultant cancer gene list; Integrate clinical data; Launch, configure, customize, and scale virtual machines (VM); Navigate and work with data sets from Cloud repositories; and Follow best practices in data and workflow management.

This 5-day workshop will cover the key bioinformatics concepts and tools required to analyze cancer genomic data sets and access and work with data sets in the Cloud. Participants will gain practical experience and skills to: Visualize genomic data; Analyze cancer –omic data for gene expression, genome rearrangement, somatic mutations, and copy number variation; Analyze and conduct pathway analysis on the resultant cancer gene list; Integrate clinical data; Launch, configure, customize, and scale virtual machines (VM); Navigate and work with data sets from Cloud repositories; and Follow best practices in data and workflow management.

Students will gain experience in: Applications and Limitations of Machine Learning and Deep Learning Decision Trees and Random Forests – how they work, how they are coded in Python and R, and how they can be used in bioinformatic applications (biomarker discovery and modeling) Artificial Neural Networks (ANNs) – how they work, how data is encoded, how they are coded in Python and R, and how they can be used in bioinformatic applications (classification and secondary structure prediction) Large Language Models (LLMs) – how they work, and how they can be used in bioinformatics applications (text mining, information extraction) Using Machine Learning tools (Decision Trees, ANNs and HMMs) on the Web (SciKit Learn and Keras/Colab)Students will gain experience in: Applications and Limitations of Machine Learning and Deep Learning Decision Trees and Random Forests – how they work, how they are coded in Python and R, and how they can be used in bioinformatic applications (biomarker discovery and modeling) Artificial Neural Networks (ANNs) – how they work, how data is encoded, how they are coded in Python and R, and how they can be used in bioinformatic applications (classification and secondary structure prediction) Large Language Models (LLMs) – how they work, and how they can be used in bioinformatics applications (text mining, information extraction) Using Machine Learning tools (Decision Trees, ANNs and HMMs) on the Web (SciKit Learn and Keras/Colab)

Participants will gain practical experience and skills to be able to: Understand the challenges and opportunities presented by complex biological data. Apply fundamental techniques for exploring and summarizing biological datasets. Address missing values through sophisticated imputation methodologies.  Analyze relationships within high-dimensional data, both between variables and samples, employing regression methods, regularization techniques, and clustering methods. Integrate various statistical methods into a cohesive workflow to tackle a variety of common problems in bioinformatics from data exploration to interpretation.