Machine Learning with Omics Data

Description:

Health research is increasingly turning to high-throughput molecular datasets (also known as ‘omic’ datasets) to discover novel biomarkers of disease risk and outcome. Unfortunately, the size and complexity of these datasets makes them difficult to manage and prone to many pitfalls. In this course, we introduce you to the latest approaches from data science for interpreting and extracting useful and reliable biomarkers from these challenging datasets.

This course aims provide an overview of the principles and methods of epidemiology and data science that are relevant to high-throughput omic studies and provide students with the knowledge and skills necessary to design and utilize population-based omic studies to gain insight and to derive robust biomarkers of exposures and health outcomes.

The course will cover:

1. examples of published omic analyses and models for epidemiological and medical applications;
2. statistical methods for preprocessing, discovering patterns and testing associations in omic datasets;
3. interpreting the biological relevance of omic patterns and associations;
4. estimating the heritability and proportion of variation explained by omic data;
5. approaches from machine learning for deriving reliable omic biomarkers for indexing exposures and predicting health outcomes; 
6. application and interpretation of appropriate metrics for evaluating biomarker performance; and
7. ethical challenges of developing, interpreting and applying molecular biomarkers.

Cost:

£750

Website and registration:

Register for this course

Region:

South West

Keywords:

Machine learning, omics,

Related publications and presentations from our eprints archive:

Machine learning

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