Network Analysis in Systems Biology
- Offered byCoursera
- Public/Government Institute
Network Analysis in Systems Biology at Coursera Overview
Duration | 30 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Network Analysis in Systems Biology at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 3 of 6 in the Systems Biology and Biotechnology Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level
- Approx. 30 hours to complete
- English Subtitles: French, Portuguese (European), Russian, English, Spanish
Network Analysis in Systems Biology at Coursera Course details
- 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.
Network Analysis in Systems Biology at Coursera Curriculum
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
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