Course Summary
Learn how to use network biology to understand complex biological systems and analyze large-scale biological data.Key Learning Points
- Understand the basics of network biology and its applications in biological research
- Learn to use various network analysis tools to analyze biological data
- Explore real-world examples of network biology in action
Job Positions & Salaries of people who have taken this course might have
- USA: $77,000
- India: ₹8,50,000
- Spain: €32,000
- USA: $77,000
- India: ₹8,50,000
- Spain: €32,000
- USA: $102,000
- India: ₹15,00,000
- Spain: €40,000
- USA: $77,000
- India: ₹8,50,000
- Spain: €32,000
- USA: $102,000
- India: ₹15,00,000
- Spain: €40,000
- USA: $87,000
- India: ₹12,00,000
- Spain: €36,000
Related Topics for further study
Learning Outcomes
- Understand the principles of network biology and how it can be applied in biological research
- Learn to use various network analysis tools to analyze biological data
- Apply network biology concepts to real-world examples
Prerequisites or good to have knowledge before taking this course
- Basic understanding of biology
- Familiarity with programming concepts
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Systems Biology and Biotechnology Specialization
- Introduction to Bioinformatics
- Genomic Data Science with Galaxy
Related Education Paths
Notable People in This Field
- Albert-László Barabási
- Uri Alon
Related Books
Description
An introduction to data integration and statistical methods used in contemporary Systems Biology, Bioinformatics and Systems Pharmacology research. The course covers methods to process raw data from genome-wide mRNA expression studies (microarrays and RNA-seq) including data normalization, differential expression, clustering, enrichment analysis and network construction. The course contains practical tutorials for using tools and setting up pipelines, but it also covers the mathematics behind the methods applied within the tools. The course is mostly appropriate for beginning graduate students and advanced undergraduates majoring in fields such as biology, math, physics, chemistry, computer science, biomedical and electrical engineering. The course should be useful for researchers who encounter large datasets in their own research. The course presents software tools developed by the Ma’ayan Laboratory (http://labs.icahn.mssm.edu/maayanlab/) from the Icahn School of Medicine at Mount Sinai, but also other freely available data analysis and visualization tools. The ultimate aim of the course is to enable participants to utilize the methods presented in this course for analyzing their own data for their own projects. For those participants that do not work in the field, the course introduces the current research challenges faced in the field of computational systems biology.
Outline
- Course Overview and Introductions
- Design Principles of Complex Systems
- Introduction to Cell Biology
- Introduction to Molecular Biology
- Course Logistics
- Grading Policy
- Resources and Links to Additional Materials
- MATLAB License
- Introduction to Complex Systems
- Introduction to Cell Biology
- Introduction to Molecular Biology
- Topological and Network Evolution Models
- Small-World and Scale-Free Networks
- Duplication-Divergence and Network Motifs
- Large Size Motifs and Complex Models of Network Evolution
- Network Properties of Biological Networks
- Rich-Get-Richer
- Duplication-Divergence and Network Motifs
- Large Size Motifs
- Topological Properties of Biological Networks
- Types of Biological Networks
- Types of Biological Networks
- Genes2Networks and Network Visualization
- Sets2Networks - Creating Functional Association Networks
- Genes2FANs - Analyzing Gene Lists with Functional Association Networks
- Types of Biological Networks
- Genes2Networks and Network Visualization
- Functional Association Networks with Sets2Networks
- Functional Association Networks with Genes2FANs
- Data Processing and Identifying Differentially Expressed Genes
- Data Normalization
- Characteristic Direction Method - Part 1
- Characteristic Direction Method - Part 2
- Characteristic Direction Method - Part 3
- Characteristic Direction Method - Part 4
- Data Normalization
- Characteristic Direction
- Gene Set Enrichment and Network Analyses
- Enrichment Analysis and Enrichr
- GEO2Enrichr: A Google Chrome Extension for Gene Set Extraction and Enrichment
- Gene Set Enrichment Analysis (GSEA) - Preliminaries
- Gene Set Enrichment Analysis (GSEA) - Part 2
- Principal Angle Enrichment Analysis (PAEA)
- Network2Canvas (N2C) and Enrichment Analysis with N2C
- Expression2Kinases: Inferring Pathways from Differentially Expressed Genes
- DrugPairSeeker and the New CMAP
- Classifying Patients/Tumors from TCGA
- GATE Desktop Software Tool
- The Fisher Exact Test and Enrichr
- Gene Set Enrichment Analysis (GSEA) - Part 1
- Gene Set Enrichment Analysis (GSEA) - Part 2
- Principal Angle Enrichment Analysis (PAEA)
- GATE and Network2Canvas
- Expression2Kinases
- DrugPairSeeker and the New CMAP
- Classifying Patients from TCGA
- Deep Sequencing Data Processing and Analysis
- RNA-seq Analysis - Preliminaries
- RNA-seq Analysis - Using TopHat and Cufflinks
- RNA-seq Analysis - R Basics
- RNA-seq Analysis - CummeRbund
- STAR: An Ultra-fast RNA-seq Aligner
- ChIP-seq Analysis - Part 1
- ChIP-seq Analysis - Part 2
- RNA-seq and UNIX/Linux Commands
- RNA-seq Pipeline
- CummeRbund and R Programming
- CummeRbund - Demo
- RNA-seq STAR
- ChIP-seq Analysis - Part 1
- ChIP-seq Analysis - Part 2
- Principal Component Analysis, Self-Organizing Maps, Network-Based Clustering and Hierarchical Clustering
- Principal Component Analysis (PCA) - Part 1
- Principal Component Analysis (PCA) - Part 2
- Principal Component Analyis (PCA) Plotting in MATLAB
- Clustergram in MATLAB
- Self-Organizing Maps
- Network-Based Clustering
- MATLAB License
- Principal Component Analysis (PCA) - Part 1
- Principal Component Analysis (PCA) - Part 2
- Principal Component Analysis (PCA) with MATLAB
- Hierarchical Clustering (HC) with MATLAB
- Self-Organizing Maps
- Network-Based Clustering
- Resources for Data Integration
- Big Data in Biology and Data Integration
- Resources for Data Integration - Part 1
- Resources for Data Integration - Part 2
- Resources for Data Integration - Part 3
- Resources for Data Integration - Part 4
- Big Data in Biology and Data Integration
- Resources for Data Integration
- Crowdsourcing: Microtasks and Megatasks
- Crowdsourcing in Bioinformatics
- Crowdsourcing Tasks for this Course
- Crowdsourcing: Microtasks and Megatasks
- Final Exam
- Final Exam
Summary of User Reviews
The Network Biology course on Coursera is a great introduction to the field of network biology. Many users found the course to be engaging and well-organized.Key Aspect Users Liked About This Course
The course provides a comprehensive overview of network biology concepts and techniques.Pros from User Reviews
- Engaging and well-organized content
- Great introduction to network biology
- Comprehensive overview of concepts and techniques
- Excellent instructors
- Useful for both beginners and advanced learners
Cons from User Reviews
- Some users found the course to be too basic
- The course does not provide hands-on experience with network biology tools
- Some sections are too technical for beginners
- Lack of interaction with instructors and other students
- Limited practical applications of the concepts taught
Avi Ma’ayan, PhD
4.5 Raiting
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