Visual Interaction, Statistical Analysis and Machine Learning to Advance Life Science Research with JMP Software (ADDED FEE) — Professional Development Computer Technology Workshop
The size and scope of biological data collection in life science applications continues to grow exponentially. Recent successes in biomarker discovery, precision medicine (notably oncology) and early clinical trial safety and efficacy detection, coupled with reductions in technology costs, have led to the generation of a wealth of biological information collected in increasingly complex experiments. Effective visualization, data exploration and manipulation are critical to assess applications of appropriate statistical methodologies. In this presentation we demonstrate using the JMP family of products (JMP, JMP Pro, JMP Genomics, and JMP Clinical) to establish pipelines for data quality assessment, exploratory statistical analysis and machine learning methods for end-to-end analysis with biological data. Using JMP products, we highlight the advantages of a visual, interactive interface to quickly assess large, complex data. Specific examples include: integrative genomic techniques, complex mixed models for longitudinal RNA-Seq analysis, feature engineering, model assessment and cross validation in machine learning, and clinical data science applications for oncology clinical trials. Effective communication and analysis reporting is also highlighted using new features to publish interactive web reports to JMP Public.
Instructor(s): Kelci Miclaus, SAS Institute/JMP Division; Ruth Hummel, SAS Institute, JMP Division