About this Course
최근 조회 6,287

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지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

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일정에 따라 마감일을 재설정합니다.

완료하는 데 약 27시간 필요

권장: 6-8 hours/week...


자막: 영어

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

완료하는 데 약 27시간 필요

권장: 6-8 hours/week...


자막: 영어

강의 계획 - 이 강좌에서 배울 내용

완료하는 데 2시간 필요

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 videos (Total 52 min), 4 readings, 3 quizzes
3개의 동영상
Introduction to Cell Biology16m
Introduction to Molecular Biology19m
4개의 읽기 자료
Course Logistics10m
Grading Policy10m
Resources and Links to Additional Materials10m
MATLAB License10m
3개 연습문제
Introduction to Complex Systems20m
Introduction to Cell Biology18m
Introduction to Molecular Biology20m
완료하는 데 2시간 필요

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 videos (Total 45 min), 4 quizzes
4개의 동영상
Duplication-Divergence and Network Motifs8m
Large Size Motifs and Complex Models of Network Evolution10m
Network Properties of Biological Networks11m
4개 연습문제
Duplication-Divergence and Network Motifs16m
Large Size Motifs16m
Topological Properties of Biological Networks18m
완료하는 데 2시간 필요

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 videos (Total 58 min), 4 quizzes
4개의 동영상
Genes2Networks and Network Visualization16m
Sets2Networks - Creating Functional Association Networks14m
Genes2FANs - Analyzing Gene Lists with Functional Association Networks14m
4개 연습문제
Types of Biological Networks16m
Genes2Networks and Network Visualization14m
Functional Association Networks with Sets2Networks16m
Functional Association Networks with Genes2FANs16m
완료하는 데 1시간 필요

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 videos (Total 41 min), 2 quizzes
5개의 동영상
Characteristic Direction Method - Part 18m
Characteristic Direction Method - Part 27m
Characteristic Direction Method - Part 310m
Characteristic Direction Method - Part 45m
2개 연습문제
Data Normalization14m
Characteristic Direction12m
완료하는 데 4시간 필요

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 videos (Total 139 min), 1 reading, 8 quizzes
9개의 동영상
GEO2Enrichr: A Google Chrome Extension for Gene Set Extraction and Enrichment7m
Gene Set Enrichment Analysis (GSEA) - Preliminaries13m
Gene Set Enrichment Analysis (GSEA) - Part 28m
Principal Angle Enrichment Analysis (PAEA)18m
Network2Canvas (N2C) and Enrichment Analysis with N2C17m
Expression2Kinases: Inferring Pathways from Differentially Expressed Genes24m
DrugPairSeeker and the New CMAP17m
Classifying Patients/Tumors from TCGA11m
1개의 읽기 자료
GATE Desktop Software Tool10m
8개 연습문제
The Fisher Exact Test and Enrichr18m
Gene Set Enrichment Analysis (GSEA) - Part 112m
Gene Set Enrichment Analysis (GSEA) - Part 210m
Principal Angle Enrichment Analysis (PAEA)10m
GATE and Network2Canvas14m
DrugPairSeeker and the New CMAP16m
Classifying Patients from TCGA16m
완료하는 데 4시간 필요

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 videos (Total 125 min), 7 quizzes
7개의 동영상
RNA-seq Analysis - Using TopHat and Cufflinks21m
RNA-seq Analysis - R Basics23m
RNA-seq Analysis - CummeRbund23m
STAR: An Ultra-fast RNA-seq Aligner13m
ChIP-seq Analysis - Part 113m
ChIP-seq Analysis - Part 212m
7개 연습문제
RNA-seq and UNIX/Linux Commands16m
RNA-seq Pipeline20m
CummeRbund and R Programming20m
CummeRbund - Demo18m
RNA-seq STAR10m
ChIP-seq Analysis - Part 118m
ChIP-seq Analysis - Part 216m
완료하는 데 3시간 필요

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 videos (Total 90 min), 1 reading, 6 quizzes
6개의 동영상
Principal Component Analysis (PCA) - Part 28m
Principal Component Analyis (PCA) Plotting in MATLAB15m
Clustergram in MATLAB14m
Self-Organizing Maps14m
Network-Based Clustering24m
1개의 읽기 자료
MATLAB License10m
6개 연습문제
Principal Component Analysis (PCA) - Part 112m
Principal Component Analysis (PCA) - Part 214m
Principal Component Analysis (PCA) with MATLAB18m
Hierarchical Clustering (HC) with MATLAB16m
Self-Organizing Maps12m
Network-Based Clustering10m
완료하는 데 1시간 필요

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 videos (Total 49 min), 2 quizzes
5개의 동영상
Resources for Data Integration - Part 110m
Resources for Data Integration - Part 212m
Resources for Data Integration - Part 39m
Resources for Data Integration - Part 410m
2개 연습문제
Big Data in Biology and Data Integration16m
Resources for Data Integration24m
완료하는 데 1시간 필요

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 videos (Total 19 min), 1 quiz
2개의 동영상
Crowdsourcing Tasks for this Course3m
1개 연습문제
Crowdsourcing: Microtasks and Megatasks16m
완료하는 데 2시간 필요

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
1개 연습문제
Final Exam1시 50분
15개의 리뷰Chevron Right


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Network Analysis in Systems Biology의 최상위 리뷰

대학: FPJun 3rd 2016

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

대학: CCApr 6th 2016

Its really a very interesting course ,and very informative



Avi Ma’ayan, PhD

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

마운트사이나이 아이칸 의과대학 정보

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....

Systems Biology and Biotechnology 전문 분야 정보

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....
Systems Biology and Biotechnology

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  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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