À propos de ce cours
4.5
119 notes
15 avis

100 % en ligne

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Approx. 27 heures pour terminer

Recommandé : 6-8 hours/week...

Anglais

Sous-titres : Anglais

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Approx. 27 heures pour terminer

Recommandé : 6-8 hours/week...

Anglais

Sous-titres : Anglais

Programme du cours : ce que vous apprendrez dans ce cours

Semaine
1
2 heures pour terminer

Course Overview and Introductions

The 'Introduction to Complex Systems' module discusses complex systems and leads to the idea that a cell can be considered a complex system or a complex agent living in a complex environment just like us. The 'Introduction to Biology for Engineers' module provides an introduction to some central topics in cell and molecular biology for those who do not have the background in the field. This is not a comprehensive coverage of cell and molecular biology. The goal is to provide an entry point to motivate those who are interested in this field, coming from other disciplines, to begin studying biology....
3 vidéos (Total 52 min), 4 lectures, 3 quiz
3 vidéos
Introduction to Cell Biology16 min
Introduction to Molecular Biology19 min
4 lectures
Course Logistics10 min
Grading Policy10 min
Resources and Links to Additional Materials10 min
MATLAB License10 min
3 exercices pour s'entraîner
Introduction to Complex Systems20 min
Introduction to Cell Biology18 min
Introduction to Molecular Biology20 min
Semaine
2
2 heures pour terminer

Topological and Network Evolution Models

In the 'Topological and Network Evolution Models' module, we provide several lectures about a historical perspective of network analysis in systems biology. The focus is on in-silico network evolution models. These are simple computational models that, based of few rules, can create networks that have a similar topology to the molecular networks observed in biological systems. ...
4 vidéos (Total 45 min), 4 quiz
4 vidéos
Duplication-Divergence and Network Motifs8 min
Large Size Motifs and Complex Models of Network Evolution10 min
Network Properties of Biological Networks11 min
4 exercices pour s'entraîner
Rich-Get-Richer14 min
Duplication-Divergence and Network Motifs16 min
Large Size Motifs16 min
Topological Properties of Biological Networks18 min
Semaine
3
2 heures pour terminer

Types of Biological Networks

The 'Types of Biological Networks' module is about the various types of networks that are typically constructed and analyzed in systems biology and systems pharmacology. This lecture ends with the idea of functional association networks (FANs). Following this lecture are lectures that discuss how to construct FANs and how to use these networks for analyzing gene lists. ...
4 vidéos (Total 58 min), 4 quiz
4 vidéos
Genes2Networks and Network Visualization16 min
Sets2Networks - Creating Functional Association Networks14 min
Genes2FANs - Analyzing Gene Lists with Functional Association Networks14 min
4 exercices pour s'entraîner
Types of Biological Networks16 min
Genes2Networks and Network Visualization14 min
Functional Association Networks with Sets2Networks16 min
Functional Association Networks with Genes2FANs16 min
Semaine
4
1 heures pour terminer

Data Processing and Identifying Differentially Expressed Genes

This set of lectures in the 'Data Processing and Identifying Differentially Expressed Genes' module first discusses data normalization methods, and then several lectures are devoted to explaining the problem of identifying differentially expressed genes with the focus on understanding the inner workings of a new method developed by the Ma'ayan Laboratory called the Characteristic Direction. ...
5 vidéos (Total 41 min), 2 quiz
5 vidéos
Characteristic Direction Method - Part 18 min
Characteristic Direction Method - Part 27 min
Characteristic Direction Method - Part 310 min
Characteristic Direction Method - Part 45 min
2 exercices pour s'entraîner
Data Normalization14 min
Characteristic Direction12 min
Semaine
5
4 heures pour terminer

Gene Set Enrichment and Network Analyses

In the 'Gene Set Enrichment and Network Analyses' module the emphasis is on tools developed by the Ma'ayan Laboratory to analyze gene sets. Several tools will be discussed including: Enrichr, GEO2Enrichr, Expression2Kinases and DrugPairSeeker. In addition, one lecture will be devoted to a method we call enrichment vector clustering we developed, and two lectures will describe the popular gene set enrichment analysis (GSEA) method and an improved method we developed called principal angle enrichment analysis (PAEA)....
9 vidéos (Total 139 min), 1 lecture, 8 quiz
9 vidéos
GEO2Enrichr: A Google Chrome Extension for Gene Set Extraction and Enrichment7 min
Gene Set Enrichment Analysis (GSEA) - Preliminaries13 min
Gene Set Enrichment Analysis (GSEA) - Part 28 min
Principal Angle Enrichment Analysis (PAEA)18 min
Network2Canvas (N2C) and Enrichment Analysis with N2C17 min
Expression2Kinases: Inferring Pathways from Differentially Expressed Genes24 min
DrugPairSeeker and the New CMAP17 min
Classifying Patients/Tumors from TCGA11 min
1 lectures
GATE Desktop Software Tool10 min
8 exercices pour s'entraîner
The Fisher Exact Test and Enrichr18 min
Gene Set Enrichment Analysis (GSEA) - Part 112 min
Gene Set Enrichment Analysis (GSEA) - Part 210 min
Principal Angle Enrichment Analysis (PAEA)10 min
GATE and Network2Canvas14 min
Expression2Kinases20 min
DrugPairSeeker and the New CMAP16 min
Classifying Patients from TCGA16 min
Semaine
6
4 heures pour terminer

Deep Sequencing Data Processing and Analysis

A set of lectures in the 'Deep Sequencing Data Processing and Analysis' module will cover the basic steps and popular pipelines to analyze RNA-seq and ChIP-seq data going from the raw data to gene lists to figures. These lectures also cover UNIX/Linux commands and some programming elements of R, a popular freely available statistical software. Note that since these lectures were developed and recorded during the Fall of 2013, it is possible that there are better tools that should be used now since the field is rapidly advancing. ...
7 vidéos (Total 125 min), 7 quiz
7 vidéos
RNA-seq Analysis - Using TopHat and Cufflinks21 min
RNA-seq Analysis - R Basics23 min
RNA-seq Analysis - CummeRbund23 min
STAR: An Ultra-fast RNA-seq Aligner13 min
ChIP-seq Analysis - Part 113 min
ChIP-seq Analysis - Part 212 min
7 exercices pour s'entraîner
RNA-seq and UNIX/Linux Commands16 min
RNA-seq Pipeline20 min
CummeRbund and R Programming20 min
CummeRbund - Demo18 min
RNA-seq STAR10 min
ChIP-seq Analysis - Part 118 min
ChIP-seq Analysis - Part 216 min
Semaine
7
3 heures pour terminer

Principal Component Analysis, Self-Organizing Maps, Network-Based Clustering and Hierarchical Clustering

This module is devoted to various method of clustering: principal component analysis, self-organizing maps, network-based clustering and hierarchical clustering. The theory behind these methods of analysis are covered in detail, and this is followed by some practical demonstration of the methods for applications using R and MATLAB....
6 vidéos (Total 90 min), 1 lecture, 6 quiz
6 vidéos
Principal Component Analysis (PCA) - Part 28 min
Principal Component Analyis (PCA) Plotting in MATLAB15 min
Clustergram in MATLAB14 min
Self-Organizing Maps14 min
Network-Based Clustering24 min
1 lectures
MATLAB License10 min
6 exercices pour s'entraîner
Principal Component Analysis (PCA) - Part 112 min
Principal Component Analysis (PCA) - Part 214 min
Principal Component Analysis (PCA) with MATLAB18 min
Hierarchical Clustering (HC) with MATLAB16 min
Self-Organizing Maps12 min
Network-Based Clustering10 min
Semaine
8
1 heures pour terminer

Resources for Data Integration

The lectures in the 'Resources for Data Integration' module are about the various types of networks that are typically constructed and analyzed in systems biology and systems pharmacology. These lectures start with the idea of functional association networks (FANs). Following this lecture are several lectures that discuss how to construct FANs from various resources and how to use these networks for analyzing gene lists as well as to construct a puzzle that can be used to connect genomic data with phenotypic data. ...
5 vidéos (Total 49 min), 2 quiz
5 vidéos
Resources for Data Integration - Part 110 min
Resources for Data Integration - Part 212 min
Resources for Data Integration - Part 39 min
Resources for Data Integration - Part 410 min
2 exercices pour s'entraîner
Big Data in Biology and Data Integration16 min
Resources for Data Integration24 min
Semaine
9
1 heures pour terminer

Crowdsourcing: Microtasks and Megatasks

The final set of lectures presents the idea of crowdsourcing. MOOCs provide the opportunity to work together on projects that are difficult to complete alone (microtasks) or compete for implementing the best algorithms to solve hard problems (megatasks). You will have the opportunity to participate in various crowdsourcing projects: microtasks and megatasks. These projects are designed specifically for this course....
2 vidéos (Total 19 min), 1 quiz
2 vidéos
Crowdsourcing Tasks for this Course3 min
1 exercices pour s'entraîner
Crowdsourcing: Microtasks and Megatasks16 min
Semaine
10
2 heures pour terminer

Final Exam

The final exam consists of multiple choice questions from topics covered in all of modules of the course. Some of the questions may require you to perform some of the analysis methods you learned throughout the course on new datasets. ...
1 quiz
1 exercices pour s'entraîner
Final Exam50 min
4.5
15 avisChevron Right

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a bénéficié d'un avantage concret dans sa carrière grâce à ce cours

50%

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Meilleurs avis

par FPJun 3rd 2016

Excellent course to get deep into the data analysis of system biology experimentation.

par CCApr 6th 2016

Its really a very interesting course ,and very informative

Enseignants

Avatar

Avi Ma’ayan, PhD

Director, Mount Sinai Center for Bioinformatics
Professor, Department of Pharmacological Sciences

À propos de Icahn School of Medicine at Mount Sinai

The Icahn School of Medicine at Mount Sinai, in New York City is a leader in medical and scientific training and education, biomedical research and patient care....

À propos de la Spécialisation Biologie des systèmes et biotechnologie

Design systems-level experiments using appropriate cutting edge techniques, collect big data, and analyze and interpret small and big data sets quantitatively. The Systems Biology Specialization covers the concepts and methodologies used in systems-level analysis of biomedical systems. Successful participants will learn how to use experimental, computational and mathematical methods in systems biology and how to design practical systems-level frameworks to address questions in a variety of biomedical fields. In the final Capstone Project, students will apply the methods they learned in five courses of specialization to work on a research project....
Biologie des systèmes et biotechnologie

Foire Aux Questions

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