Network Analysis in Systems Biology

Mount Sinai develops network analysis through biological examples and data-processing methods. Differential gene expression and enrichment lead into sequencing analysis, dimensionality reduction and clustering. Resources for integrating data extend the workflow. This is a more analytical continuation than a general introduction to systems biology.

What you will learn

• Describe different types of biological networks.
• Relate gene-expression analysis to enrichment methods.
• Compare clustering and data-integration approaches.

Who this suits

Intermediate learners; detailed prerequisites are not listed. Preview the expected biological data, statistics and computational methods.

Before you enroll

Treat this as an academic biology course. Check the expected scientific vocabulary and methods before enrolling; a conceptual or data exercise does not replace supervised laboratory training.

Coursera offers paid enrollment or subscription access. Check the enrollment screen for assessment and certificate terms, trials and any financial-aid options. Provider access terms can change; review them before paying.

Put it into practice

Use a small public example network to identify a highly connected node. Explain why connectivity alone cannot establish its biological importance.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at Coursera.

How YellowCourse describes and checks learning resources.