For bioinformatians

  

For biologists

  

Classes

  

For undergraduates

  
Analyses of next-generation genomic data (차세대유전체데이터분석, 대학원)

This course will provide opportunities for students to learn how to analyze sequence data from conventional low-throughput (usually long) sequences to high-throughput next-generation sequences (usually short) that come from genomes and transcriptomes. Students will learn how to identify genomic variations and disease-causal factors, how to profile expression level of all isoforms, how to interpret biological meanings and insights from results, and etc. The requirement for this class is a basic computer programming experience and a basic knowledge of biology and genomics.

For graduates

  

For all

  
Sequence analysis algorithms (서열분석알고리즘, 대학원)

This course will provide opportunities for students to learn fundamental concepts for computational sequence analysis and to learn basic and advanced computational, statistical algorithms to analyze DNA, RNA, and protein sequences. Student will also practice various computational, statistical methods for sequence analyses and visualization. This course will also deal with various NGS data including cancer genomes, transcriptomes, small and long non-coding RNAs, and epigenomes. The requirement for this class is an experience of computer programming and a basic knowledge of biology and genomics.

BioData-AI (바이오데이터-AI, 대학원)

To be posted

Programming in Bioinformatics (생물정보프로그래밍, 대학원)

This course will provide opportunities for students to learn shared concepts, language and skills which biologists must have to operate in a collaborative, inter-disciplinary mode. The goal is to educate bio-related students (from biology, biotechnology and medicine) who have no or little experience in bioinformatics, computational biology, and computer programming. The requirement for this class is a basic or advanced knowledge of biology and genomcis.

RNA Genomics (RNA 유전체학, 대학원 )

This course will provide opportunities for students to learn about classes, biogenesis, genomic features, and functional roles of non-coding RNAs. This class will cover two classes of regulatory noncoding RNAs that comprise small noncoding RNAs (microRNAs, endogenous siRNAs, and Piwi-associated RNAs) and long noncoding RNAs (mRNA-like lncRNAs, antisense ncRNAs, circular RNAs, eRNAs and repeat-associated RNAs). Students will also review current hot research topics in the research field of regulatory noncoding RNAs. The requirement for this class is a basic knowledge of molecular biology and genomics.

BioData Science (바이오데이터과학, 학부)

To be posted

Computational biology (전산생물학, 학부)

This class will provide opportunities for students to learn shared concepts, language and skills which bioinformaticians must have to operate in a collaborative, interdisciplinary mode. The goal is to educate nonbio-related students (from computer science, informatics, eletrical and mechanical engineering, and other natural sciences) who have no or little knowledge in biology and genomics. The requirement for this class is a basic programming skill.

Bioinformatics (생물정보학, 학부)

This class will provide opportunities for students to learn shared concepts, language and skills which bioinformaticians must have to operate in a collaborative, interdisciplinary mode. The goal is to educate nonbio-related students (from computer science, informatics, eletrical and mechanical engineering, and other natural sciences) who have no or little knowledge in biology and genomics. The requirement for this class is a basic programming skill.

Off/online Courses

"Computational genomics" "Transcriptome analyses"

So you want to be a computational biologist?    by Nick Loman2 & Mick Watson

Computing: Out of the hood    by Roberta Kwok

Please reload

For who want to be computational biologists  or bioinformaticians

Machine learning & Deep Learning courses

[Machine Learning Lectures]

https://www.youtube.com/playlist?list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy

 

1. Intro

https://youtu.be/HgBpFaATdoA 

https://youtu.be/ukzFI9rgwfU

 

2. ML vs DL vs AI

https://youtu.be/9dFhZFUkzuQ

3. Supervised vs unsupervised 

https://youtu.be/kE5QZ8G_78c

4. ML Part1

https://youtu.be/DWsJc1xnOZo

ML Part2

https://youtu.be/_Wkx_447zBM

 

5. Linear Regression

https://youtu.be/NUXdtN1W1FE

https://youtu.be/2Sb1Gvo5si8

 

6. Logistic regression

https://youtu.be/XnOAdxOWXWg

https://youtu.be/XycruVLySDg

 

7. Decision Tree

https://youtu.be/RmajweUFKvM

https://youtu.be/HmEPCEXn-ZM

 

8. Random Forest

https://youtu.be/eM4uJ6XGnSM

https://youtu.be/HeTT73WxKIc

 

9. KNN

https://youtu.be/4HKqjENq9OU

 

10. SVM

https://youtu.be/TtKF996oEl8

https://youtu.be/QkAmOb1AMrY

 

11. Naive Bayes

https://youtu.be/l3dZ6ZNFjo0

 

12. K-means clustering

https://youtu.be/Xvwt7y2jf5E

 

13. Hierarchical clustering

https://youtu.be/9U4h6pZw6f8

 

14. NN

https://youtu.be/VB1ZLvgHlYs

 

15. ML tutorial

https://youtu.be/G7fPB4OHkys

https://youtu.be/Q59X518JZHE

 

 

[DL lectures]

https://youtu.be/6M5VXKLf4D4

https://youtu.be/FbxTVRfQFuI

 

1. CNN tutorial

https://youtu.be/Jy9-aGMB_TE

 

2. RNN tutorial

https://youtu.be/lWkFhVq9-nc

 

3. DL & Tensorflow tutorial

https://youtu.be/E8n_k6HNAgs

https://youtu.be/skf35x1lNV4

https://youtu.be/6EALSPZZagg

 

4. DL & Keras tutorial

https://youtu.be/5qCDzaOUCWA

https://youtu.be/Y1-hQdgftMQ

 

5. DL & Pytorch

https://www.youtube.com/playlist?list=PLQVvvaa0QuDdeMyHEYc0gxFpYwHY2Qfdh

 

#Introduction to Algorithms

Source. MIT, 6.046J, Profs. CharlesLeiserson and Erik Demaine (Fall 2005)Link. http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-introduction-to-algorithms-sma-5503-fall-2005

+Provider description. "This courseteaches techniques for the design andanalysis of efficient algorithms, emphasizingmethods useful in practice. Topicscovered include: sorting; search trees,heaps, and hashing; divide-and-conquer;dynamic programming; amortized analysis;graph algorithms; shortest paths;network flow; computational geometry;number-theoretic algorithms; polynomialand matrix calculations; caching; andparallel computing."

 

#Artificial Intelligence

Source. Berkeley, CS 188, Prof.Pieter Abbeel (Spring 2012)Link. http://itunes.apple.com/WebObjects/MZStore.woa/wa/viewPodcast?id=496298636

+Provider description. "Basic ideasand techniques underlying the design ofintelligent computer systems. Topics includeheuristic search, problem solving,game playing, knowledge representation,logical inference, planning, reasoning underuncertainty, expert systems, learning,perception, language understanding."

+Commentary. Bioinformatics has along tradition relating it to artificialintelligence (AI), including the origins ofthe Intelligent Systems for MolecularBiology conference series. Besides introducingmachine learning, which should bepursued further in the next course listed,this course introduces knowledge representation,important as a foundation forbiological ontologies; Bayesian nets, usefulin biological network causal analysis; andnatural language understanding, which ishighly relevant to biomedical text mining.The course uses Python, and refers to butdoes not require the very popular text byBerkeley Prof. Stuart Russell and Google징짱sPeter Norvig, "Artificial Intelligence: AModern Approach"

Online courses

[Data Science tutorial]

https://youtu.be/ua-CiDNNj30

 

[R tutorial]

https://youtu.be/_V8eKsto3Ug

 

 

[Python online 교육]

1. Python tutorial

https://youtu.be/rfscVS0vtbw

https://www.youtube.com/playlist?list=PLFD32AF85033E6DDC

 

2. Python 3 w/ Pandas

https://www.youtube.com/playlist?list=PLQVvvaa0QuDfSfqQuee6K8opKtZsh7sA9

# Data science for high-throughput sequencing

 http://data-science-sequencing.github.io/

#NHGRI Genomics Lectures

http://www.genome.gov/27552686

 

#Genetics

Source. Berkeley, PMB 160, Profs.Robert Fischer and Jennifer Fletcher(Spring 2012)

+Provider description. "A considerationof plant genetics and molecularbiology. Principles of nuclear and organellargenome structure and function:regulation of gene expression in responseto environmental and developmental stimuli;clonal analysis; investigation of themolecular and genetic bases for the exceptionalcellular and developmental strategiesadopted by plants."Link. http://webcast.berkeley.edu/playlist#c,d,PMB,2B7E0C3DBF1D43ED

 

#Molecular Biology

Source. Berkeley, MCB110, Profs.Thomas Alber, Qiang Zhou and QingZhong (Fall 2009)Link. http://itunes.apple.com/WebObjects/MZStore.woa/wa/viewPodcast?id=354820440

+Provider description. "Molecularbiology of prokaryotic and eukaryotic cellsand their viruses. Mechanisms of DNAreplication, transcription, translation. Structureof genes and chromosomes. Regulation of gene expression. Biochemical processesand principles in membrane structure andfunction, intracellular trafficking andsubcellular compartmentation, cytoskeletalarchitecture, nucleocytoplasmic transport,signal transduction mechanisms, and cellcycle control

 

#Eukarytotic gene expression

Source. Indian Institute of Science(IISc), Bangalore, Prof. P.N. RangarajanLink. http://nptel.iitm.ac.in/courses/104108056

+Provider description. "[Topicsinclude] cis-acting elements and transactingfactors 징짝 domain structure ofeukaryotic transcription factors 징짝 role of chromatin 징짝 synthesis of mRNA, rRNA,and tRNA 징짝 cell surface receptors 징짝intracellular receptors 징짝 regulatioression during development 징짝recombinant protein expression systems징짝 gene therapy and transgenictechnology 징짝"

 

#Computational Molecular Biology

Source. Stanford, Biochem 218, Prof.Doug Brutlag (Spring 2012)Link. http://biochem218.stanford.edu

+Provider description. "징짝 apractical, hands-on approach to the fieldof computational molecular biology. Thecourse is recommended for both molecularbiologists and computer scientists desiringto understand the major issues concerninganalysis of genomes, sequences andstctures."

 

#Computational Biology

Source. Stony Brook University, CSE549, Prof. Steven Skiena (2010)Link. http://www.algorithm.cs.sunysb.edu/computationalbiology

+Provider description. "This coursefocuses on current problems in computationalbiology and bioinformatics. Ouremphasis will be algorithmic, on discoveringappropriate combinatorial algorithmproblems and the techniques to solvethem. Primary topics will include DNAsequence assembly, DNA/protein sequenceassembly, DNA/protein sequence comparison,hybridization array analysis, RNA andprotein folding, and phylogenic trees."

+Commentary. This course providesa computer scientist징짱s approach to computationalbiology, and is thus listed separatelyfrom a corresponding course in theBiology Department. The emphasis here ismore on how the algorithms work than onhow to use them. Prof. Skiena징짱s groundis algorithms and discrete math,and he uses the book "An Introduction toBioinformatics Algorithms" by Neil Jonesand Prof. Pavel Pevzner of the Universityof California at San Diego [37].

 

#Introduction to Genome Science

Source. University of Pennsylvaniaon Coursera, Profs. John Hogenesch andJohn Isaac Murray (Fall 2012)Link. https://www.coursera.org/course/genomescience

+Provider description. "This courseserves as an introduction to the main laboratoryand theoretical aspects of genomicsand is divided into themes: genomes,genetics, functional genomics, systemsbiology, single cell approaches, proteomics,and applications. We start with the basics,DNA sequencing and the genome project,then move to high throughput sequencingmethods and applications. Next weintroduce principles of genetics and thenapply them in clinical genetics and otherlarge-scale sequencing projects. In thefunctional genomics unit, we start withRNA expression dynamics, analysis ofalternative splicing, epigenomics and ChIPseq,and metagenomics. Model organismsand forward and reverse genetics screens arethen discussed, along with quantitative traitlocus (QTL) and eQTL analysis. After that,we introduce integrative and single cellgenomics approaches and systems biology.Finally, we conclude by introducing 징짝proteomic approaches

 

#Current Topics in Genome Analysis

Source. National Human GenomeResearch Institute (Winter 2012)Link. http://www.genome.gov/12514288s

+Provider description. "A lectureseries covering contemporary areas ingenomics and bioinformatics."

 

#Biological Seminars

Source. Howard Hughes MedicalInstitute, iBioSeminarsLink. http://www.ibioseminars.org

+Provider description. "iBioSeminarsis a freely available library of video seminarsfrom outstanding scientists, including manyHHMI investigators. These lectures, whichdescribe on-going research in leadinglaboratories, feature an extensive introductionto the subject matter, making themaccessible to advanced undergraduates orbeginning graduate students and researchersoutside of the specific field.Themain subjectareas are biologicalmechanisms, cell biology

 

#Linear Algebra

Source. MIT, 18.06SC, Prof. GilbertStrang (Fall 2011)Link. http://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011

+Provider description. "This coursecovers matrix theory and linear algebra,emphasizing topics useful in other disciplinessuch as physics, economics and socialsciences, natural sciences, and engineering."$

+Commentary. Prof. Strang is a legendas an educator, charmingly diffidentin his delivery yet never lacking in clarity.He has long held that the subject of linearalgebra should be given as much or moreteaching emphasis than calculus anddifferential equations, and the rise of BigData is now proving him correct beyondany doubt. No bioinformatics professionaldealing with high-dimensional data canafford to neglect an understanding ofmatrix math, with many bioinformaticsmethods currently making use of variousmatrix factorizations, transformations,decompositions, and eigenwhatevers.

 

#Statistics

Source. Princeton on Coursera, Prof.Andrew Conway (Fall 2012)Link. https://www.coursera.org/course/stats1 +Provider description. "StatisticsOne is designed to be a friendly introductionto very simple, very basic, fundamentalconcepts in statistics 징짝 Randomsampling and assignment. Distributions 징짝Descriptive statistics. Measurement 징짝Correlation. Causality 징짝 Multiplion. Ordinary least squares 징nfidence intervals. Statistical power징ests, chi-square tests. Analysis of Variance.징짱

 

#Information Theory

Source. Stanford ClassX, EE376A,Prof. Tom Cover (Winter 2011)Link. http://171.64.93.201/ClassX/system/users/web/pg/view_subject.php?subject=EE376A_WINTER_2010_2011

+Provider description. "The fundamentalideas of information theory.Entropy and intrinsic randomness. Datacompression to the entropy limit. Huffmancoding. Arithmetic coding. Channel capacity,the communication limit. Gaussianchannels. Kolmogorov complexity. Asymptoticequipartition property. Informationtheory and Kelly gambling.Applications to communication and datacompression."

+Commentary. It goes without sayingthat much of molecular biology deals withthe storage and transmission ofinformation, which by itself makesinformation theory a proper topic ofstudy for bioinformatics.

 

#Introduction to Computer Scienceand Programming

Source. MIT, 6.00SC, Prof. JohnGuttag (Fall 2008)Link. http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-00sc-introduction-to-computer-scienceand-programming-spring-2011

+Provider description. "This subjectis aimed at students with little or noprogramming experience. It aims toprovide students with an understandingof the role computation can play in solvingproblems. It also aims to help students,regardless of their major, to feel justifiablyconfident of their ability to write smallprograms that allow them to accomplishuseful goals. The class will use the Pythonprogramming language."

 

#Data Structures

Source. Berkeley, Computer Science61B, Prof. Paul Hilfinger (Fall 2011)Link. http://webcast.berkeley.edu/playlist#c,d,Computer_Science,63AE13B304CE443E

+Provider description. "Fundamentaldynamic data structures, includinglinear lists, queues, trees, and otherlinked structures; arrays, strings, and hashtables. Storage management. Elementaryprinciples of software engineering. Abstract data types. Algorithms for sortingand searching. Introduction to the Javaprogramming language."

 

#Introduction to Algorithms

Source. MIT, 6.046J, Profs. CharlesLeiserson and Erik Demaine (Fall 2005)Link. http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-introduction-to-algorithms-sma-5503-fall-2005

+Provider description. "This courseteaches techniques for the design andanalysis of efficient algorithms, emphasizingmethods useful in practice. Topicscovered include: sorting; search trees,heaps, and hashing; divide-and-conquer;dynamic programming; amortized analysis;graph algorithms; shortest paths;network flow; computational geometry;number-theoretic algorithms; polynomialand matrix calculations; caching; andparallel computing."

 

#Artificial Intelligence

Source. Berkeley, CS 188, Prof.Pieter Abbeel (Spring 2012)Link. http://itunes.apple.com/WebObjects/MZStore.woa/wa/viewPodcast?id=496298636

+Provider description. "Basic ideasand techniques underlying the design ofintelligent computer systems. Topics includeheuristic search, problem solving,game playing, knowledge representation,logical inference, planning, reasoning underuncertainty, expert systems, learning,perception, language understanding."

+Commentary. Bioinformatics has along tradition relating it to artificialintelligence (AI), including the origins ofthe Intelligent Systems for MolecularBiology conference series. Besides introducingmachine learning, which should bepursued further in the next course listed,this course introduces knowledge representation,important as a foundation forbiological ontologies; Bayesian nets, usefulin biological network causal analysis; andnatural language understanding, which ishighly relevant to biomedical text mining.The course uses Python, and refers to butdoes not require the very popular text byBerkeley Prof. Stuart Russell and Google징짱sPeter Norvig, "Artificial Intelligence: AModern Approach"

© 2014 by BIG Lab. All copyrights reserved. Editor: JWNAM

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