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CompbioCourse.Main History
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* Jan 31: [[CompbioCourse/Assign1 | Assignment 1]] has been posted. Due date is 11th Feb, just before midnight
* Jan 24: Check out the neat video [[http://www.studiodaily.com/2006/07/cellular-visions-the-inner-life-of-a-cell/#ooid=pvNWIxMjqDgM2ekFDBz6jy4u1W-i1zji | Cellular Visions: Inner Life of a Cell]] and the other animations at [[http://vcell.ndsu.edu/animations/ | Virtual Cell]].
* Jan 24: Check out the neat video [[http://www.studiodaily.com/2006/07/cellular-visions-the-inner-life-of-a-cell/#ooid=pvNWIxMjqDgM2ekFDBz6jy4u1W-i1zji | Cellular Visions: Inner Life of a Cell]] and the other animations at [[http://vcell.ndsu.edu/animations/ | Virtual Cell]].
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[l] Database Search and Significance
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[l] HMMs (%red% Guest Lecture: Prof. Chris Bystroff %%)
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[l] HMMs (%red% Guest Lecture: [[http://www.bioinfo.rpi.edu/bystrc/ | Prof. Chris Bystroff]] %%)
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[l] Profile HMMs (%red% Guest Lecture: Prof. Chris Bystroff %%)
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[l] Profile HMMs (%red% Guest Lecture: [[http://www.bioinfo.rpi.edu/bystrc/ | Prof. Chris Bystroff]] %%)
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Computational Biology and Bioinformatics are essentially interchangeable terms, referring to the science of analyzing biological data. The goal of this course is to introduce the main topics and the frontiers of computational biology. The basic topics include sequence and protein structure analysis (alignment, evolution, search, motifs, and indexing). The emerging topics include gene expression analysis, network biology, and kernel data mining methods. The emphasis will be on the application of these methods to the various "omics" within computational systems biology, i.e., genomics, proteomics, interactomics, transcriptomics, and metabolomics.
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Computational Biology and Bioinformatics are essentially interchangeable terms, referring to the science of analyzing biological data. The goal of this course is to introduce the main topics and the frontiers of computational biology. The basic topics include sequence and protein structure analysis (alignment, evolution, search, motifs, and indexing). The emerging topics include next generation sequencing, gene expression analysis, network biology, and kernel data mining methods. The emphasis will be on the application of these methods to the various "omics" within computational systems biology, i.e., genomics, proteomics, interactomics, transcriptomics, and metabolomics.
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* Jan 24: Check out the neat video [[http://www.studiodaily.com/main/searchlist/6850.html#ooid=pvNWIxMjqDgM2ekFDBz6jy4u1W-i1zji| Cellular Visions: Inner Life of a Cell]] and the other animations at [[http://vcell.ndsu.edu/animations/ | Virtual Cell]].
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* Jan 24: Check out the neat video [[http://www.studiodaily.com/2006/07/cellular-visions-the-inner-life-of-a-cell/#ooid=pvNWIxMjqDgM2ekFDBz6jy4u1W-i1zji | Cellular Visions: Inner Life of a Cell]] and the other animations at [[http://vcell.ndsu.edu/animations/ | Virtual Cell]].
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!!%center% CSCI-4964/6964: Bioinformatics & Computational Biology, Spring 2012
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!!%center% CSCI-4964/6964: Bioinformatics & Computational Biology, Spring 2013
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* Jan 22: Course website is up, with the calendar and syllabus.
* Jan25: Check the CompbioCourse/Reading]] section to lookup the latest reading materials.
* Jan 26Check out the neat video [[http://www.studiodaily.com/main/searchlist/6850.html#ooid=pvNWIxMjqDgM2ekFDBz6jy4u1W-i1zji| Cellular Visions: Inner Life of a Cell]] and the other animations at [[http://vcell.ndsu.edu/animations/ | Virtual Cell]].
* Jan 31: [[CompbioCourse/Assign1 | Assignment 1]] has been posted.
* Feb 12: [[CompbioCourse/Assign2 | Assignment 2]] has been posted.
* Feb 26: [[CompbioCourse/Assign3 | Assignment 3]] has been posted.
* Mar 23: [[CompbioCourse/Assign4 | Assignment 4]] has been posted.
* Apr 14: [[CompbioCourse/Assign5 | Assignment 5]] has been posted.
* Apr 24: [[CompbioCourse/Assign6 | Assignment 6]] has been posted.
* Jan
* Jan 26
* Jan 31: [[CompbioCourse/Assign1 | Assignment 1]] has been posted.
* Feb 12: [[CompbioCourse/Assign2 | Assignment 2]] has been posted.
* Feb 26: [[CompbioCourse/Assign3 | Assignment 3]] has been posted.
* Mar 23: [[CompbioCourse/Assign4 | Assignment 4]] has been posted.
* Apr 14: [[CompbioCourse/Assign5 | Assignment 5]] has been posted.
* Apr 24: [[CompbioCourse/Assign6 | Assignment 6]] has been posted
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* Jan 21: Course website is up, with the calendar and syllabus.
* Jan 24: Check out the neat video [[http://www.studiodaily.com/main/searchlist/6850.html#ooid=pvNWIxMjqDgM2ekFDBz6jy4u1W-i1zji| Cellular Visions: Inner Life of a Cell]] and the other animations at [[http://vcell.ndsu.edu/animations/ | Virtual Cell]].
* Jan 24: Check out the neat video [[http://www.studiodaily.com/main/searchlist/6850.html#ooid=pvNWIxMjqDgM2ekFDBz6jy4u1W-i1zji| Cellular Visions: Inner Life of a Cell]] and the other animations at [[http://vcell.ndsu.edu/animations/ | Virtual Cell]].
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[l] Sequence Alignment Scoring Matrices & Database Searching
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* Apr 24: [[CompbioCourse/Assign6 | Assignment 6]] has been posted.
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[l] Network Science
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* Apr 14: [[CompbioCourse/Assign5 | Assignment 5]] has been posted.
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* Mar 23: [[CompbioCourse/Assign4 | Assignment 4]] has been posted.
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* Feb 26: [[CompbioCourse/Assign3 | Assignment 3]] has been posted.
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* Feb 12: [[CompbioCourse/Assign2 | Assignment 2]] has been posted.
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* Jan 31: [[CompbioCourse/Assign1 | Assignment 1]] has been posted.
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* Jan 26: Check out the neat video [[http://www.studiodaily.com/main/searchlist/6850.html#ooid=pvNWIxMjqDgM2ekFDBz6jy4u1W-i1zji| Cellular Visions: Inner Life of a Cell]] and the other animations at [[http://vcell.ndsu.edu/animations/ | Virtual Cell]].
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[l] Significance & Multiple Sequence Alignment
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* Jan 25: Check the [[CompbioCourse/Readings]] section to lookup the latest reading materials.
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* Jan 25: Check the [[CompbioCourse/Reading]] section to lookup the latest reading materials.
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* Jan 25: Check the [[CompbioCourse/Readings]] section to lookup the latest reading materials.
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[!c] Reading
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[l] [[(Attach:)intro.ppt]]
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[[http://www.python.org | Python]], perl.org | Perl]], or [[http://www.r-project.org | R]]. Only these three scripting languages will be permitted for the assignments.
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[[http://www.python.org | Python]], or [[http://www.r-project.org | R]]. Only these two scripting languages will be permitted for the assignments.
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* [[http://biozon
HW assignments will be assigned from the text. Additional reading materials will be posted to supplement any chapters, where needed. The following books are also good references:
* '''Biological Sequence Analysis''', Durbin, Eddy, Krogh, Mitchison, Cambridge University Press, 1999
* '''Protein Bioinformatics''', Eidhammer, Jonassen, Taylor, John Wiley & Sons, 2004
* '''Understanding Bioinformatics''', Zvelebil, Baum, Garland Science, 2007
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There is no required text for the course. Reading materials will be posted online.
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* Assignments (40%): There will be two types of assignments: homework questions from the book, and practically oriented assignments. For the latter you'll be asked to implement algorithms and apply them to real datasets, to complement the theory. Only python, perl, or R are permitted for the scripting language.
* Exams (60%): There will be three exams covering the main topics of the course. The tentative examschedule is posted on the class schedule table. There is no comprehensive final exam. All exams are open book.
* Exams (60%): There will be three exams covering the main topics of the course. The tentative exam
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* Assignments (40%): There will be two types of assignments: homework questions from the book, and practically oriented assignments. For the latter you'll be asked to implement algorithms and apply them to real datasets, to complement the theory. Only python, and R are permitted for the scripting language.
* Exams (60%): There will be three exams covering the main topics of the course. The tentative exam dates are posted on the class schedule table. There is no comprehensive final exam. All exams are open book.
* Exams (60%): There will be three exams covering the main topics of the course. The tentative exam dates are posted on the class schedule table. There is no comprehensive final exam. All exams are open book.
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'''Laptop Policy''': No laptops or other electronic devices is permitted during lectures. You may however use these during exams to access course material online, or to use the calculator functions. Browsing the web for solutions, etc. is of course not permitted. Scripting (using perl, python or R) is also not permitted to solve the exam questions, which are intended to be done by hand.
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'''Laptop Policy''': No laptops or other electronic devices are permitted during lectures. You may however use these during exams to access course material online, or to use the calculator functions. Browsing the web for solutions, etc. is of course not permitted. Scripting (using python or R or other languages) is also not permitted to solve the exam questions, which are intended to be done by hand.
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!!%center% CSCI-4964/6964: Computational Biology & Bioinformatics, Spring 2011
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!!%center% CSCI-4964/6964: Bioinformatics & Computational Biology, Spring 2012
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'''Class''': 10-11:50AM, MR, Low 4034\\
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* Apr 16: [[Assign6 | Assignment 6]] has been posted. Due date: 5th May (thurs), before midnight.
* Apr 16: [[Assign5 | Assignment 5]] has been posted. Due date: 25th Apr (mon), before midnight.
* Mar 22: [[Assign4 | Assignment 4]] has been posted. Due date: 8th Apr (fri), before midnight.
* Mar 8: [[Assign3 | Assignment 3]] has been posted. Due date: 24th Mar (thurs), before midnight.
* Feb 4: [[Assign1 | Assignment 1]] has been posted. Due date: 14th Feb (mon), before midnight.
* Jan 25: Readings from the book are indicated with each topic in the calendar below.
* Jan 23: Course website is up, with the calendar and syllabus.
* Apr 16: [[Assign5 | Assignment 5]] has been posted. Due date: 25th Apr (mon), before midnight.
* Mar 22: [[Assign4 | Assignment 4]] has been posted. Due date: 8th Apr (fri), before midnight.
* Mar 8: [[Assign3 | Assignment 3]] has been posted. Due date: 24th Mar (thurs), before midnight.
* Feb 4: [[Assign1 | Assignment 1]] has been posted. Due date: 14th Feb (mon), before midnight.
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* Jan 23
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* Jan 22: Course website is up, with the calendar and syllabus.
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[l] [[(Attach:)Lecture5.pdf | Lecture 5]]
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* Feb 4: [[Assign1 | Assignment 1]] has been posted. Due date: 14th Feb (mon), before midnight.
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[l] [[(Attach:)Lecture2.pdf | Lecture 2]]
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* Jan 25: Readings from the book are indicated with each topic in the calendar below.
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[l] [[(Attach:)intro.ppt | Molecular Biology Overview]]
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[l] Phylogenetics (parsimony)
[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture6.pdf|Lecture 6]]
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[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture7b.pdf | Lecture 7]]
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[l] Phylogenetics (probabilistic)
[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture8.pdf | Lecture 8]]
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[l] Overview
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'''Laptop Policy''': No laptops or other electronic devices is permitted during lectures. You may however use these during exams to access course material online, or to use the calculator functions. Browsing the web for solutions, etc. is of course not permitted.
to:
'''Laptop Policy''': No laptops or other electronic devices is permitted during lectures. You may however use these during exams to access course material online, or to use the calculator functions. Browsing the web for solutions, etc. is of course not permitted. Scripting (using perl, python or R) is also not permitted to solve the exam questions, which are intended to be done by hand.
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Computational Biology deals with the science of analyzing biological data. The goal of this course is to introduce the main topics and the frontiers of computational biology. The basic topics include sequence and protein structure analysis (alignment, evolution, search, motifs, and indexing). The emerging topics include gene expression analysis, network biology, and kernel data mining methods. The emphasis will be on the application of these methods to the "omics" subfields of computational systems biology, i.e., genomics, proteomics, interactomics, transcriptomics, and metabolomics.
to:
Computational Biology and Bioinformatics are essentially interchangeable terms, referring to the science of analyzing biological data. The goal of this course is to introduce the main topics and the frontiers of computational biology. The basic topics include sequence and protein structure analysis (alignment, evolution, search, motifs, and indexing). The emerging topics include gene expression analysis, network biology, and kernel data mining methods. The emphasis will be on the application of these methods to the various "omics" within computational systems biology, i.e., genomics, proteomics, interactomics, transcriptomics, and metabolomics.
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The pre-requisites for this course include data structures and algorithms, discrete mathematics, and probability & statistics. Knowledge of basic linear algebra will serve you well too. Assignments will require the use of the [[http://www.r-project.org | R]] or [[http://www.perl.org | Perl]] or [[http://www.python.org | Python]]. Only these three scripting languages will be permitted for the assignments, which must be submitted online at the wiki site. Knowledge of [[http://www.pmwiki.org/| pmwiki]] markup usage will be your responsibility.
to:
The pre-requisites for this course include data structures and algorithms, discrete mathematics, and probability & statistics. Knowledge of basic linear algebra will serve you well too. Assignments will require the use of
[[http://www.python.org | Python]], [[http://www.perl.org | Perl]], or [[http://www.r-project.org | R]]. Only these three scripting languages will be permitted for the assignments.
[[http://www.python.org | Python]], [[http://www.perl.org | Perl]], or [[http://www.r-project.org | R]]. Only these three scripting languages will be permitted for the assignments.
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The following books are
to:
The required text for the course is
* [[http://biozon.org/proteomics/index.html | Introduction to Computational Proteomics]], Golan Yona, CRC Press, 2010.
HW assignments will be assigned from the text. Additional reading materials will be posted to supplement any chapters, where needed. The following books are also good references:
* [[http://biozon.org/proteomics/index.html | Introduction to Computational Proteomics]], Golan Yona, CRC Press, 2010.
HW assignments will be assigned from the text. Additional reading materials will be posted to supplement any chapters, where needed. The following books are also good references:
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Your grade will be a combination of the following items. Note that the final distribution is subject to some change depending on the number of assignments, but exams will be at least 60%.
* Assignments (40%): The assignments are meantto be practically oriented. You'll be asked to implement algorithms and apply them to real datasets, to complement the theory. Only R, perl or python is permitted for the scripting language. There will be roughly one assignment every two week, to be submitted via the course wiki site. User accounts will be created after first day of class.
* Exams (60%): There will be three exams covering the main topics of the course. The tentative exam schedule is posted on the class schedule table. There is no comprehensive final exam.
* Assignments (40%): The assignments are meant
* Exams (60%): There will be three exams covering the main topics of the course. The tentative exam schedule is posted on the class schedule table. There is no comprehensive final exam
to:
Your grade will be a combination of the following items.
* Assignments (40%): There will be two types of assignments: homework questions from the book, and practically oriented assignments. For the latter you'll be asked to implement algorithms and apply them to real datasets, to complement the theory. Only python, perl, or R are permitted for the scripting language.
* Exams (60%): There will be three exams covering the main topics of the course. The tentative exam schedule is posted on the class schedule table. There is no comprehensive final exam. All exams are open book.
* Assignments (40%): There will be two types of assignments: homework questions from the book, and practically oriented assignments. For the latter you'll be asked to implement algorithms and apply them to real datasets, to complement the theory. Only python, perl, or R are permitted for the scripting language.
* Exams (60%): There will be three exams covering the main topics of the course. The tentative exam schedule is posted on the class schedule table. There is no comprehensive final exam. All exams are open book.
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'''Laptop Policy''': No laptops or other electronic devices is permitted during lectures. You may however use these during exams to access course material online, or to use the calculator functions. Browsing the web for solutions, etc. is of course not permitted.
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!!%center% CSCI-4964/6964: Computational Biology, Spring 2010
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!!%center% CSCI-4964/6964: Computational Biology & Bioinformatics, Spring 2011
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'''Class''': 10-11:50AM, MR, AE 216\\
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'''Class''': 10-11:50AM, MR, Low 4034\\
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* Apr 25: [[CompbioCourse/Assign5 | Assignment 5]] posted.
* Apr 13: [[CompbioCourse/Assign4 | Assignment 4]] posted.
* Mar 17: [[CompbioCourse/Assign3 | Assignment 3]] posted.
* Mar 15: reading material on HMMs posted.
* Feb 23: [[http://www.cs.rpi.edu/~zaki/Courses/bioinfo/Spring10/pmwiki.php/CompbioCourse/Reading | Readings]] on multiple sequence alignment and phylogenetics posted.
* Feb 21: [[CompbioCourse/Assign2 | Assignment 2]] posted.
* Feb 10: [[CompbioCourse/Assign1 | Assignment 1]] posted.
* Feb 8: New reading material posted for alignment, scoring matrices and database search
* Feb 6: Accounts created for all student on the assignment submission site
* Jan 31: Course website is up, with the calendar and syllabus.
* Apr 13: [[CompbioCourse/Assign4 | Assignment 4]] posted.
* Mar 17: [[CompbioCourse/Assign3 | Assignment 3]] posted.
* Mar 15: reading material on HMMs posted.
* Feb 23: [[http://www.cs.rpi.edu/~zaki/Courses/bioinfo/Spring10/pmwiki.php/CompbioCourse/Reading | Readings]] on multiple sequence alignment and phylogenetics posted.
* Feb 21: [[CompbioCourse/Assign2 | Assignment 2]] posted.
* Feb 10: [[CompbioCourse/Assign1 | Assignment 1]] posted.
* Feb 8: New reading material posted for alignment, scoring matrices and database search
* Feb 6: Accounts created for all student on the assignment submission site
* Jan 31
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* Jan 23: Course website is up, with the calendar and syllabus.
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[l]Network Clustering
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[l]Microarray Analysis
[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture23.pdf | Lecture 23]]
[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture23.pdf | Lecture 23]]
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[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture21.pdf | Lecture 21]]
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[l]Network Clustering
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[l]Network Clustering (spectral)
[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture22.pdf | Lecture 22]]
[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture22.pdf | Lecture 22]]
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[l]Network Biology
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[l]Network Centrality and Motifs
[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture20.pdf | Lecture 20]]
[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture20.pdf | Lecture 20]]
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[l]Network Clustering
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* Apr 25: [[CompbioCourse/Assign5 | Assignment 5]] posted.
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[l]Network Biology (Graph Models)
[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture19.pdf | Lecture 19]]
[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture19.pdf | Lecture 19]]
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[l]Network Biology
[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture18.pdf | Lecture 18]]
[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture18.pdf | Lecture 18]]
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* Apr 13: [[CompbioCourse/Assign4 | Assignment 4]] posted.
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[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture17.pdf | Lecture 17]]
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[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture16.pdf | Lecture 16]]
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[l] Gene Finding
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[l]Sequence Assembly
[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture15.pdf | Lecture 15]]
[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture15.pdf | Lecture 15]]
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[l]Network Biology Basics
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[l]Genome Rearrangement
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[l]Network Motifs
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[l]Microarray Analysis
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[l]Network Clustering
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[l]Microarray Analysis
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[l]Network Clustering
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[l]Network Biology
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[l]Microarray Analysis
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[l]Network Biology
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[l]Network Motifs
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[l]Kernel Methods in Computational Biology
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[l]Network Clustering
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[l]Kernel Methods
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[l]Network Clustering
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[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture14.pdf | Lecture 14]]
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[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture13.pdf | Lecture 13]]
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[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture12.pdf | Lecture 12]]
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[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture11.pdf | Lecture 11]]
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[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture10.pdf | Lecture 10]]
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* Mar 17: [[CompbioCourse/Assign3 | Assignment 3]] posted.
* Mar 15: reading material on HMMs posted.
* Mar 15: reading material on HMMs posted.
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[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture9.pdf | Lecture 9]]
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[l]Sequence Patterns & Indexing (Suffix Trees)
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[l]Hidden Markov Models
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[l]Hidden Markov Models
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[l]Sequence Indexing (Suffix Trees)
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[l]Structure Alignment
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[l]Sequence Indexing (Suffix Arrays)
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[l]Structure Motifs
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[l]Sequence Motifs
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[l]Structure Indexing
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[l]Sequence Motifs
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[l]Sequence Patterns & Indexing (Suffix Trees)
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[l] Phylogenetics (probabilistic)
[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture8.pdf | Lecture 8]]
[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture8.pdf | Lecture 8]]
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[l]Sequence Motifs
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[l]Sequence Patterns & Indexing (Suffix Trees)
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[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture7.pdf|Lecture 7]]
to:
[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture7b.pdf | Lecture 7]]
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[l] Phylogenetics
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[l] Phylogenetics (distance-based)
[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture7.pdf|Lecture 7]]
[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture7.pdf|Lecture 7]]
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[l] [Path:/~zaki/Courses/bioinfo/lectures/Lecture6.pdf|Lecture 6]]
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[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture6.pdf|Lecture 6]]
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[l] Phylogenetics
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[l] Phylogenetics (parsimony)
[l] [Path:/~zaki/Courses/bioinfo/lectures/Lecture6.pdf|Lecture 6]]
[l] [Path:/~zaki/Courses/bioinfo/lectures/Lecture6.pdf|Lecture 6]]
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* Feb 23: [[http://www.cs.rpi.edu/~zaki/Courses/bioinfo/Spring10/pmwiki.php/CompbioCourse/Reading | Readings]] on multiple sequence alignment and phylogenetics posted.
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* Feb 21: [[CompbioCourse/Assign2 | Assignment 2]] posted.
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[l]Sequence Indexing (Suffix Trees)
to:
[l]Multiple Sequence Alignment
[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture5.pdf|Lecture 5]]
[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture5.pdf|Lecture 5]]
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[l]Sequence Indexing (Suffix Arrays)
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[l] Phylogenetics
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[l] Phylogenetics
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[l]Hidden Markov Models
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[l]Sequence Patterns & Indexing (Suffix Trees)
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[l]Sequence Motifs
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[l]Phylogenetics
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[l]Hidden Markov Models
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[l]Structure Alignment
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[l]Hidden Markov Models
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[l]Structure Prediction
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[l]Structure Alignment
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[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture4.pdf|Lecture 4]]
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* Feb 10: [[CompbioCourse/Assign1 | Assignment 1]] posted.
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[l]Sequence Searching
to:
[l]Sequence Alignment & Scoring
[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture3.pdf|Lecture 3]]
[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture3.pdf|Lecture 3]]
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[l]Sequence Scoring
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[l]Sequence Scoring & Searching
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* Feb 8: New reading material posted for alignment, scoring matrices and database search
* Feb 6: Accounts created for all student on the assignment submission site
* Feb 6: Accounts created for all student on the assignment submission site
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[l] [[Path:/~zaki/Courses/bioinfo/lectures/Lecture2.pdf|Lecture 2]]
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[l] [[Path:~/zaki/Courses/bioinfo/lectures/lec1-intro.ppt|Intro.ppt]]
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[l] [[Path:/~zaki/Courses/bioinfo/lectures/lec1-intro.ppt|Intro.ppt]]
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[!c]Lectures
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[l] [[Path:~/zaki/Courses/bioinfo/lectures/lec1-intro.ppt|Intro.ppt]]
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* Biological Sequence Analysis, Durbin, Eddy), Krogh, Mitchison, Cambridge University Press, 1999
* Protein Bioinformatics: An Algorithmic Approach to Sequence and Structure Analysis, Eidhammer, Jonassen, Taylor, John Wiley & Sons, 2004
* Understanding Bioinformatics, Zvelebil, Baum, Garland Science, 2007
* Protein Bioinformatics
* Understanding Bioinformatics
to:
* '''Biological Sequence Analysis''', Durbin, Eddy, Krogh, Mitchison, Cambridge University Press, 1999
* '''Protein Bioinformatics''', Eidhammer, Jonassen, Taylor, John Wiley & Sons, 2004
* '''Understanding Bioinformatics''', Zvelebil, Baum, Garland Science, 2007
* '''Protein Bioinformatics''', Eidhammer, Jonassen, Taylor, John Wiley & Sons, 2004
* '''Understanding Bioinformatics''', Zvelebil, Baum, Garland Science, 2007
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Computational Biology deals with the science of analyzing biological data. The goal of this course is to introduce the main topics and the frontiers of computational biology. The basic topics include sequence and protein structure analysis (alignment, evolution, search, motifs, and indexing). The emerging topics include gene expression analysis, network biology, and kernel data mining methods. The emphasis will be on the application of these methods to the "omics" subfields of systems biology, i.e., computational genomics, proteomics, interactomics, transcriptomics, and metabolomics.
to:
Computational Biology deals with the science of analyzing biological data. The goal of this course is to introduce the main topics and the frontiers of computational biology. The basic topics include sequence and protein structure analysis (alignment, evolution, search, motifs, and indexing). The emerging topics include gene expression analysis, network biology, and kernel data mining methods. The emphasis will be on the application of these methods to the "omics" subfields of computational systems biology, i.e., genomics, proteomics, interactomics, transcriptomics, and metabolomics.
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to:
Computational Biology deals with the science of analyzing biological data. The goal of this course is to introduce the main topics and the frontiers of computational biology. The basic topics include sequence and protein structure analysis (alignment, evolution, search, motifs, and indexing). The emerging topics include gene expression analysis, network biology, and kernel data mining methods. The emphasis will be on the application of these methods to the "omics" subfields of systems biology, i.e., computational genomics, proteomics, interactomics, transcriptomics, and metabolomics.
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* knowledgeable about the fundamental data mining tasks like pattern mining, classification and clustering
to:
* knowledgeable about the fundamental computational biology tasks like sequence and structure analysis and evolution, biological networks, and data mining methods in bioinformatics
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* able to implement and apply the techniques to real world datasets
to:
* able to implement and apply the techniques to real world omics datasets
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The pre-requisites for this course include data structures and algorithms and discrete mathematics.
Basics of linear algebra, and probability & statistics will be very useful as well. Assignments will require the use of the [[http://www.r-project.org | R software]]. Students are expected to learn R on their own. Assignments must be submitted online at the wiki site. Knowledge of [[http://www.pmwiki.org/| pmwiki]] markup usage will be your responsibility.
Basics of linear algebra, and probability & statistics will be very useful as
to:
The pre-requisites for this course include data structures and algorithms, discrete mathematics, and probability & statistics. Knowledge of basic linear algebra will serve you well too. Assignments will require the use of the [[http://www.r-project.org | R]] or [[http://www.perl.org | Perl]] or [[http://www.python.org | Python]]. Only these three scripting languages will be permitted for the assignments, which must be submitted online at the wiki site. Knowledge of [[http://www.pmwiki.org/| pmwiki]] markup usage will be your responsibility.
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There is no required text for the course. Notes will be handed out in class.
The following text books are also good references:
*Introduction to Data Mining, by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Addison Wesley, 2006.
* Data Mining: Concepts and Techniques (2nd edition), by Jiawei Han and Micheline Kamber, Morgan Kaufmann, 2006.
The following text
*
* Data Mining: Concepts and Techniques (2nd edition), by Jiawei Han and Micheline Kamber
to:
There is no required text for the course. Reading materials will be handed out via the course wiki.
The following books are good references:
* Biological Sequence Analysis, Durbin, Eddy), Krogh, Mitchison, Cambridge University Press, 1999
* Protein Bioinformatics: An Algorithmic Approach to Sequence and Structure Analysis, Eidhammer, Jonassen, Taylor, John Wiley & Sons, 2004
* Understanding Bioinformatics, Zvelebil, Baum, Garland Science, 2007
The following books are good references:
* Biological Sequence Analysis, Durbin, Eddy), Krogh, Mitchison, Cambridge University Press, 1999
* Protein Bioinformatics: An Algorithmic Approach to Sequence and Structure Analysis, Eidhammer, Jonassen, Taylor, John Wiley & Sons, 2004
* Understanding Bioinformatics, Zvelebil, Baum, Garland Science, 2007
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* Assignments (40%): The assignments are meant to be practically oriented. You'll be asked to run some mining methods on some real datasets, or to implement some algorithms, to complement the theory. There will be roughly one assignment per week, to be submitted via the course wiki site. User accounts will be created after first day of class.
to:
* Assignments (40%): The assignments are meant to be practically oriented. You'll be asked to implement algorithms and apply them to real datasets, to complement the theory. Only R, perl or python is permitted for the scripting language. There will be roughly one assignment every two week, to be submitted via the course wiki site. User accounts will be created after first day of class.
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You may consult other members of the class on the homeworks, but you must submit your own work. Anytime you borrow material from the web or elsewhere, you must acknowledge the source.
to:
You may consult other members of the class on the homeworks, but this must be limited to the ''ideas'' only; you must submit your own implementation and work. Anytime you borrow material from the web or elsewhere, you must acknowledge the source.
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[l]%red% ''EXAM I''
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[l]Hidden Markov Models
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[l]Hidden Markov Models
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[l]%red% '''EXAM I'''%%
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[l]Phylogenetics
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[l]Phylogenetics
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[l]Structure Prediction
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[l]Structure Motifs
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[l]Structure Indexing
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[l]%red% '''EXAM II'''%%
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[l]Network Biology Overview
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[l]Network Biology Basics
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[l]Network Motifs
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[l]Network Clustering
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[l]Network Clustering
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[l]Microarray Analysis
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[l]Microarray Analysis
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[l]Kernel Methods in Computational Biology
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[l]%red% '''EXAM III'''%%
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[l]Kernel Methods
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[l] Overview of Biology
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[l]Sequence Alignment
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[l]Sequence Searching
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[l]Sequence Scoring
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[l]Sequence Indexing (Suffix Trees)
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[l]Sequence Indexing (Suffix Arrays)
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[l]Sequence Motifs
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[l]%red% ''EXAM I''
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[l]Hidden Markov Models
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!!%center% CSCI-4964/6964: Computational Biology, Spring 2010
\\
>>cframe text-align=left width=40pct<<
'''Class''': 10-11:50AM, MR, AE 216\\
'''Instructor Office Hours''': 12-1PM, MR
>><<
\\
----
!!!%red%Announcements%%
(:table border=1 bgcolor=aliceblue width=100%:)
(:cell:)
(:div style="height: 200px; overflow: auto; text-align: justify; padding-top: 10px; padding-left:10px; padding-right:10px;" :)
* Jan 31: Course website is up, with the calendar and syllabus.
(:divend:)
(:tableend:)
\\
----
!!!Calendar
A tentative sequence of topics to be covered in the classes; changes are likely as the course progresses.
[table border=1 width=100%]
[row bgcolor=lavender]
[!c]Day: Date
[!c]Topic
----
[row]
[l]M: Jan 25
[l]%red% NO CLASS %%
[row bgcolor=aliceblue]
[l]R: Jan 28
[l]%red% NO CLASS %%
------
[row]
[l]M: Feb 1
[l]
[row bgcolor=aliceblue]
[l]R: Feb 4
[l]
------
[row]
[l]M: Feb 8
[l]
[row bgcolor=aliceblue]
[l]R: Feb 11
[l]
------
[row]
[l]M: Feb 15
[l]%red% NO CLASS (president's day) %%
[row bgcolor=aliceblue]
[l]R: Feb 18
[l]
------
[row]
[l]M: Feb 22
[l]
[row bgcolor=aliceblue]
[l]R: Feb 25
[l]
------
[row]
[l]M: Mar 1
[l]
[row bgcolor=aliceblue]
[l]R: Mar 4
[l]
------
[row]
[l]M: Mar 8
[l]%red% NO CLASS (spring break) %%
[row bgcolor=aliceblue]
[l]R: Mar 11
[l]%red% NO CLASS (spring break) %%
------
[row]
[l]M: Mar 15
[l]
[row bgcolor=aliceblue]
[l]R: Mar 18
[l]
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[row]
[l]M: Mar 22
[l]
[row bgcolor=aliceblue]
[l]R: Mar 25
[l]
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[row]
[l]M: Mar 29
[l]
[row bgcolor=aliceblue]
[l]R: Apr 1
[l]
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[row]
[l]M: Apr 5
[l]
[row bgcolor=aliceblue]
[l]R: Apr 8
[l]
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[row]
[l]M: Apr 12
[l]
[row bgcolor=aliceblue]
[l]R: Apr 15
[l]
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[row]
[l]M: Apr 19
[l]
[row bgcolor=aliceblue]
[l]R: Apr 22
[l]
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[row]
[l]M: Apr 26
[l]
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[l]R: Apr 29
[l]
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[row]
[l]M: May 3
[l]
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[l]R: May 6
[l]
------
[row]
[l]M: May 10
[l]
[row bgcolor=aliceblue]
[l]R: May 13
[l] %red% NO CLASS (reading days) %%
------
[tableend]
\\
----
\\
!!!Syllabus
(:table border=1 bgcolor=aliceblue width=100%:)
(:cell:)
(:div style="height: 400px; overflow: auto; text-align: justify; padding-top: 10px; padding-left:10px; padding-right:10px;" :)
!!!!!Introduction
Data mining is the process of automatic discovery of patterns, models, changes, associations and anomalies in massive databases. This course will provide an introduction to the main topics in data mining and knowledge discovery, including: statistical foundations, pattern mining, classification, and clustering. Emphasis will be laid on the algorithmic foundations.
!!!!!Learning Objectives
After taking this course students will be
* knowledgeable about the fundamental data mining tasks like pattern mining, classification and clustering
* able to understand the key algorithms for the main tasks
* able to implement and apply the techniques to real world datasets
!!!!!Prerequisites
The pre-requisites for this course include data structures and algorithms and discrete mathematics.
Basics of linear algebra, and probability & statistics will be very useful as well. Assignments will require the use of the [[http://www.r-project.org | R software]]. Students are expected to learn R on their own. Assignments must be submitted online at the wiki site. Knowledge of [[http://www.pmwiki.org/| pmwiki]] markup usage will be your responsibility.
!!!!!Textbook
There is no required text for the course. Notes will be handed out in class.
The following text books are also good references:
* Introduction to Data Mining, by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Addison Wesley, 2006.
* Data Mining: Concepts and Techniques (2nd edition), by Jiawei Han and Micheline Kamber, Morgan Kaufmann, 2006.
!!!!!Grading Policy
Your grade will be a combination of the following items. Note that the final distribution is subject to some change depending on the number of assignments, but exams will be at least 60%.
* Assignments (40%): The assignments are meant to be practically oriented. You'll be asked to run some mining methods on some real datasets, or to implement some algorithms, to complement the theory. There will be roughly one assignment per week, to be submitted via the course wiki site. User accounts will be created after first day of class.
* Exams (60%): There will be three exams covering the main topics of the course. The tentative exam schedule is posted on the class schedule table. There is no comprehensive final exam.
'''Attendance''': Students are strongly encouraged to participate in the class, and should try to attend all classes.
!!!!!Academic Integrity
You may consult other members of the class on the homeworks, but you must submit your own work. Anytime you borrow material from the web or elsewhere, you must acknowledge the source.
The school takes cases of academic dishonesty very seriously, resulting in an automatic "F" grade for the course. Students should familiarize themselves with the relevant portion of the [[http://www.rpi.edu/dept/doso/2008-2010RPIStudentHandbook.pdf | Rensselaer Handbook of Student Rights and Responsibilities]] on this topic.
(:divend:)
(:tableend:)
\\
>>cframe text-align=left width=40pct<<
'''Class''': 10-11:50AM, MR, AE 216\\
'''Instructor Office Hours''': 12-1PM, MR
>><<
\\
----
!!!%red%Announcements%%
(:table border=1 bgcolor=aliceblue width=100%:)
(:cell:)
(:div style="height: 200px; overflow: auto; text-align: justify; padding-top: 10px; padding-left:10px; padding-right:10px;" :)
* Jan 31: Course website is up, with the calendar and syllabus.
(:divend:)
(:tableend:)
\\
----
!!!Calendar
A tentative sequence of topics to be covered in the classes; changes are likely as the course progresses.
[table border=1 width=100%]
[row bgcolor=lavender]
[!c]Day: Date
[!c]Topic
----
[row]
[l]M: Jan 25
[l]%red% NO CLASS %%
[row bgcolor=aliceblue]
[l]R: Jan 28
[l]%red% NO CLASS %%
------
[row]
[l]M: Feb 1
[l]
[row bgcolor=aliceblue]
[l]R: Feb 4
[l]
------
[row]
[l]M: Feb 8
[l]
[row bgcolor=aliceblue]
[l]R: Feb 11
[l]
------
[row]
[l]M: Feb 15
[l]%red% NO CLASS (president's day) %%
[row bgcolor=aliceblue]
[l]R: Feb 18
[l]
------
[row]
[l]M: Feb 22
[l]
[row bgcolor=aliceblue]
[l]R: Feb 25
[l]
------
[row]
[l]M: Mar 1
[l]
[row bgcolor=aliceblue]
[l]R: Mar 4
[l]
------
[row]
[l]M: Mar 8
[l]%red% NO CLASS (spring break) %%
[row bgcolor=aliceblue]
[l]R: Mar 11
[l]%red% NO CLASS (spring break) %%
------
[row]
[l]M: Mar 15
[l]
[row bgcolor=aliceblue]
[l]R: Mar 18
[l]
------
[row]
[l]M: Mar 22
[l]
[row bgcolor=aliceblue]
[l]R: Mar 25
[l]
------
[row]
[l]M: Mar 29
[l]
[row bgcolor=aliceblue]
[l]R: Apr 1
[l]
------
[row]
[l]M: Apr 5
[l]
[row bgcolor=aliceblue]
[l]R: Apr 8
[l]
------
[row]
[l]M: Apr 12
[l]
[row bgcolor=aliceblue]
[l]R: Apr 15
[l]
------
[row]
[l]M: Apr 19
[l]
[row bgcolor=aliceblue]
[l]R: Apr 22
[l]
------
[row]
[l]M: Apr 26
[l]
[row bgcolor=aliceblue]
[l]R: Apr 29
[l]
------
[row]
[l]M: May 3
[l]
[row bgcolor=aliceblue]
[l]R: May 6
[l]
------
[row]
[l]M: May 10
[l]
[row bgcolor=aliceblue]
[l]R: May 13
[l] %red% NO CLASS (reading days) %%
------
[tableend]
\\
----
\\
!!!Syllabus
(:table border=1 bgcolor=aliceblue width=100%:)
(:cell:)
(:div style="height: 400px; overflow: auto; text-align: justify; padding-top: 10px; padding-left:10px; padding-right:10px;" :)
!!!!!Introduction
Data mining is the process of automatic discovery of patterns, models, changes, associations and anomalies in massive databases. This course will provide an introduction to the main topics in data mining and knowledge discovery, including: statistical foundations, pattern mining, classification, and clustering. Emphasis will be laid on the algorithmic foundations.
!!!!!Learning Objectives
After taking this course students will be
* knowledgeable about the fundamental data mining tasks like pattern mining, classification and clustering
* able to understand the key algorithms for the main tasks
* able to implement and apply the techniques to real world datasets
!!!!!Prerequisites
The pre-requisites for this course include data structures and algorithms and discrete mathematics.
Basics of linear algebra, and probability & statistics will be very useful as well. Assignments will require the use of the [[http://www.r-project.org | R software]]. Students are expected to learn R on their own. Assignments must be submitted online at the wiki site. Knowledge of [[http://www.pmwiki.org/| pmwiki]] markup usage will be your responsibility.
!!!!!Textbook
There is no required text for the course. Notes will be handed out in class.
The following text books are also good references:
* Introduction to Data Mining, by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Addison Wesley, 2006.
* Data Mining: Concepts and Techniques (2nd edition), by Jiawei Han and Micheline Kamber, Morgan Kaufmann, 2006.
!!!!!Grading Policy
Your grade will be a combination of the following items. Note that the final distribution is subject to some change depending on the number of assignments, but exams will be at least 60%.
* Assignments (40%): The assignments are meant to be practically oriented. You'll be asked to run some mining methods on some real datasets, or to implement some algorithms, to complement the theory. There will be roughly one assignment per week, to be submitted via the course wiki site. User accounts will be created after first day of class.
* Exams (60%): There will be three exams covering the main topics of the course. The tentative exam schedule is posted on the class schedule table. There is no comprehensive final exam.
'''Attendance''': Students are strongly encouraged to participate in the class, and should try to attend all classes.
!!!!!Academic Integrity
You may consult other members of the class on the homeworks, but you must submit your own work. Anytime you borrow material from the web or elsewhere, you must acknowledge the source.
The school takes cases of academic dishonesty very seriously, resulting in an automatic "F" grade for the course. Students should familiarize themselves with the relevant portion of the [[http://www.rpi.edu/dept/doso/2008-2010RPIStudentHandbook.pdf | Rensselaer Handbook of Student Rights and Responsibilities]] on this topic.
(:divend:)
(:tableend:)