Main

• Jan 31: Assignment 1 has been posted. Due date is 11th Feb, just before midnight
• Jan 24: Check out the neat video Cellular Visions: Inner Life of a Cell and the other animations at Virtual Cell.

[l] Lecture2.pdf

[l] Database Search and Significance

[l] R3

[l] Alignment & Scoring Matrices [l] R4, R5

[l] Significance and Database Search [l] R6, R7

[l] Lecture1.pdf

[l] R3, R4

[l] R5, R6

[l] HMMs ( Guest Lecture: Prof. Chris Bystroff )

[l] Profile HMMs ( Guest Lecture: Prof. Chris Bystroff )

[l] T (Tuesday): Feb 19

[l] Alignment Significance

[l] Multiple Sequence Alignment

[l] HMMs ( Guest Lecture: Prof. Chris Bystroff )

[l] Profile HMMs ( Guest Lecture: Prof. Chris Bystroff )

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.

• Jan 24: Check out the neat video Cellular Visions: Inner Life of a Cell and the other animations at Virtual Cell.

[l] NO CLASS (Martin Luther King Holiday)

[l] HMMs

[l] Suffix Trees and Arrays

[l] [l]

[l] EXAM I [row bgcolor=aliceblue] [l]R: Feb 28

[l] [l]

[l] [l]

[l] [l]

[l] [l]

[l] [l]

[l] [l]

[l] EXAM II [l] [l] [row bgcolor=aliceblue] [l]R: Apr 4

[l] [l]

[row] [l]M: Apr 8 [l] Gene Expression Clustering

[l]R: Apr 11 [l] Kernel Methods for Bioinfo [l] [l]

[l]M: Apr 15 [l] Kernels for Sequences [l] [l]

[l]R: Apr 18 [l] Kernel-based Classification [l] [l]

[l]M: Apr 22 [l] Kernel-based Clustering [l] [l]

[l]R: Apr 25 [l] Network Motifs -- Transcription Networks [l] [l]

[l]M: Apr 29 [l] Network Motifs -- Transcription/Signaling Networks [l] [l]

[l]R: May 2 [l] Signaling Networks [l] [l]

[l] Sequence Alignment [l] [l]

[l] Scoring Matrices & Database Searching [l] [l]

[row] [l]M: Feb 4

[l] [l] [row bgcolor=aliceblue] [l]R: Feb 7

[l] [l]

[row] [l]M: Feb 11

[l] [l] [row bgcolor=aliceblue] [l]R: Feb 14 [l] Suffix Trees [l] [l]

[l]T: Feb 19 note Tuesday [l] Suffix Arrays

CSCI-4964/6964: Bioinformatics & Computational Biology, Spring 2013

• Jan 21: Course website is up, with the calendar and syllabus.
• Jan 24: Check out the neat video Cellular Visions: Inner Life of a Cell and the other animations at Virtual Cell.

[l]M: Jan 21 [l] Martin Luther King Holiday [l] [l]

[l]R: Jan 24 [l] Introduction [l] R1, R2 [l] intro.ppt

[l]M: Jan 28 [l] Sequence Alignment Scoring Matrices & Database Searching

[l]R: Jan 31

[l]M: Feb 4

[l]R: Feb 7

[l]M: Feb 11

[l]R: Feb 14

[l]M: Feb 18

[l]R: Feb 21

[l]M: Feb 25

[l]R: Feb 28

[l]M: Mar 4

[l]R: Mar 7

[l]M: Mar 11

[l]R: Mar 14

[l]M: Mar 18

[l]R: Mar 21

[l]M: Mar 25

[l]R: Mar 28

[l]M: Apr 1

[l]R: Apr 4

[l]M: Apr 8

[l]R: Apr 11

[l]M: Apr 15

[l]R: Apr 18

[l]M: Apr 22

[l]R: Apr 25

[l]M: Apr 29

[l]R: May 2

[l]M: May 6

[l]R: May 9

[l] Lecture25.PDF

[l] Signaling Networks

[l] [l] Lecture25.PDF

[l] R32 [l] Lecture24.PDF

• Apr 24: Assignment 6 has been posted.

[l] Network Motifs -- Transcription Networks [l] R31 [l] Lecture23.PDF

[l] Network Motifs -- Transcription/Signaling Networks

[l] PPI Networks

[l] Lecture22.PDF

[l] R28, R29

[l] R30 [l] Lecture23.PDF

[l] Lecture21.PDF

[l] R28

[l] Lecture20.PDF

[l] Kernel-based Classification

[l] Kernel-based Clustering

[l] Network Science

[l] Network Models

• Apr 14: Assignment 5 has been posted.

[l] Network Biology

[l] Network Models

[l] Network Motifs

[l] Kernel Methods for Bioinfo [l] R26 [l] Lecture19.PDF

[l] Kernels for Sequences [l] R27

[l] Lecture18.PDF

[l] R24, R25

[l] R23 [l] Lecture17.PDF

[l] R24

[l] Lecture16.PDF

[l] R21 [l] Lecture15.PDF

[l] R22

• Mar 23: Assignment 4 has been posted.

[l] Lecture14.PDF

[l] R17, R18

[l] R19, R20

[l] Protein Structure & Alignment

[l] Lecture13.PDF

[l] Lecture12.PDF

[l] Genome Rearrangements & Phylogenetic Trees [l] R15 [l] Lecture11.PDF

[l] R16

[l] R13, R14 [l] Lecture10.PDF

• Feb 26: Assignment 3 has been posted.

[l] R13

[l] R12

[l]

[l] Motif Discovery

[l] Lecture9.PDF

[l] Genome Scale Alignment & Genome Rearrangements

[l] Genome Rearrangements

[l] Phylogenetic Trees

[l] Lecture8.PDF

[l] R10 [l] Lecture7.PDF

[l] R11 [l]

[l] Suffix Trees

[l] Suffix Arrays

[l] Lecture7.PDF

[l] Genome Scale Alignment

• Feb 12: Assignment 2 has been posted.

[l] Lecture6.PDF

(:tableend:)

[l] R8

[l] Lecture5.PDF

[l] R4, R5, R7

[l] R6, R7

[l] R8, R9

[l] Significance & Multiple Sequence Alignment

[l] Lecture4.PDF

[l] Profile HMMs

[l] HMMs

[l] Motifs

[l] Genome Scale Alignment

[l] Suffix Trees & Arrays

[l] Genome Rearrangements

[l] Suffix Trees & Genome Alignment

[l] Suffix Trees & Arrays

[l] Genome Rearrangements

[l] Genome Scale Analysis

[l] Multiple Sequence Alignment & Profile HMMs

[l] HMMs

[l] PSI-BLAST, Motifs

[l] Motifs

• Jan 31: Assignment 1 has been posted.

[l] Lecture3.PDF

[l] R4, R5, R6

[l] Sequence Alignment

[l] Scoring Matrices & Database Searching [l] R4, R5

[l] Lecture2.PDF

• Jan 26: Check out the neat video Cellular Visions: Inner Life of a Cell and the other animations at Virtual Cell.

[l] Significance & Multiple Sequence Alignment

[l] Profiles & PSI-BLAST

[l] Profile HMMs

[l] Hidden Markov Models

[l] Genome Rearrangements

[l] Suffix Trees & Genome Alignment

[l] Suffix Trees & Arrays

[l] Motif Discovery

[l] Motif Discovery

[l] Phylogenetic Trees

• Jan 25: Check the Reading section to lookup the latest reading materials.

• Jan 25: Check the Readings section to lookup the latest reading materials.

[l] R4

[l] Sequence Alignment & Scoring Matrices [l] R3

[l] Database Searching

[l]

[l] Significance & Multiple Sequence Alignment

[l] Profiles & PSI-BLAST

[l] Profile HMMs

[l] Hidden Markov Models

[l] Motif Discovery

[l] Suffix Trees

[l] Suffix Trees & Genome Alignment

[!c] Readings

[l] R1, R2 [l] intro.ppt

Python, or R. Only these two scripting languages will be permitted for the assignments.

There is no required text for the course. Reading materials will be posted online.

• 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.

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.

[l] [l]

[l]

[l] [l]

[l] [l]

[l] [l]

[l] [l]

[l] [l]

[l] [l]

[l] [l]

[l] [l]

[l] [l]

[l]

[l] [l]

[l]

[l] [l]

[l]

[l] [l]

[l] [l]

[l] [l]

[l]

[l] [l]

[l] [l]

[l] [l]

[l] [l]

[l] [l]

[l]M: Jan 23

[l]R: Jan 26

[l]M: Jan 30

[l]R: Feb 2

[l]M: Feb 6

[l]R: Feb 9

[l]M: Feb 13

[l]R: Feb 16

[l]M: Feb 20

[l]R: Feb 23

[l]M: Feb 27

[l]R: Mar 1

[l]M: Mar 5

[l]R: Mar 8

[l]M: Mar 12

[l]R: Mar 15

[l]M: Mar 19

[l]R: Mar 22

[l]M: Mar 26

[l]R: Mar 29

[l]M: Apr 2

[l]R: Apr 5

[l]M: Apr 9

[l]R: Apr 12

[l]M: Apr 16

[l]R: Apr 19

[l]M: Apr 23

[l]R: Apr 26

[l]M: Apr 30

[l]R: May 3

[l]M: May 7

[l]R: May 10

CSCI-4964/6964: Bioinformatics & Computational Biology, Spring 2012

Class: 10-11:50AM, MR, Low 3130\\

• Jan 22: Course website is up, with the calendar and syllabus.

[l] Lecture 24

[l] Lecture 23

[l] [l] Lecture 23

[l] R18, R19

[l] Network Clustering

• Apr 16: Assignment 6 has been posted. Due date: 5th May (thurs), before midnight.

[l] Network Models

[l] Lecture 22

[l] Network Motifs and Clustering

[l] Network Models and Motifs

[l] Network Clustering

[l] Network Classification

[l] ch 13-14, R17

[l] Lecture 21

[l]

[l]

[l]

[l] Lecture 20

[l] SVD, Gene biClustering [l] [l] Lecture 19

[row] [l]M: Apr 25

[row bgcolor=aliceblue] [l]R: Apr 28 [l] Network Centralities [l] ch 10 [l]

[l]M: May 2 [l] Network Motifs [l] ch 14

[l]R: May 5 [l] Network Clustering [l] ch 15

[l] PCA/SVD [l] ch 11, R16 [l] Lecture 19

• Apr 16: Assignment 5 has been posted. Due date: 25th Apr (mon), before midnight.

[l] ch 12, R14, R15

[l] Lecture 18

[row] [l]M: Apr 18 [l] Network Biology [l] ch 13-14

[l] Lecture 17

• Mar 22: Assignment 4 has been posted. Due date: 8th Apr (fri), before midnight.

[l] Lecture 16

[l] Gene Expression Clustering [l] Ch 10,12

• Mar 22: Assignment 4 has been posted. Due date: 6th Apr (wed), before midnight.

[l] ch 8, R13 [l] Lecture 15

[l] ch 12

[l] ch 8, R12

[l] Lecture 14

[l] Structure Alignment

[l] Protein Structure Prediction

[l] Protein Structure Alignment [l] ch 8 [l] Lecture 13

• Mar 22: Assignment 4 has been posted. Due date: 6th Apr (mon), before midnight.

[l] Ch 10 [l]

[row] [l]M: Apr 11 [l] EXAM II

• Mar 22: Assignment 4 has been posted. Due date: 4th Apr (mon), before midnight.

[l]

[row bgcolor=aliceblue] [l]R: Apr 7 [l] EXAM II

[l] R10, R11

[l] Suffix Trees & Arrays [l] R10 [l] Lecture 12

[l] ch 5, R8, R9

[l] Lecture 11

• Mar 8: Assignment 3 has been posted. Due date: 24th Mar (thurs), before midnight.

[l] ch 5, R7

[l] NO CLASS [l]

[l] Motifs, Suffix Trees & Genome Alignment

[l] Motif Discovery & Suffix Trees

[l] Suffix Trees & Genome Alignment

[l] Lecture 9

[l] Motif Discovery [l] ch 5 [l] Lecture 10

[l] ch 6 [l]

[l] Suffix Trees/Arrays [l]

[l] Motif Discovery [l] ch 5

[l] Ch 6

[l] Ch 5

[l] ch 5

[l] Hidden Markov Models

[l] Motif Discovery

[l] EXAM I

[l] Motif Discovery

[l] Suffix Trees/Arrays

[l] Significance & Multiple Sequence Alignment

[l] Profiles & PSI-BLAST

[l] Multiple Sequence Alignment

[l] Lecture 6

[l] Profiles [l] ch 4 [l] Lecture 7

[l] Profile HMMs [l] ch 6 [l] Lecture 8

[l] ch 3, R5, R6

[l] ch 3, R4 [l]

[l] Alignments & Scoring Matrices

[l] R4

[l] Scoring Matrices

[l] Database Searching [l] ch 3 [l] Lecture 5

[l] Multiple Sequence Alignment & Profiles

• Feb 4: Assignment 1 has been posted. Due date: 14th Feb (mon), before midnight.

[l] ch 3, R4

[l] Lecture 3

[l] Alignment & Scoring Matrices

[l] Lecture 3

[l] Scoring Matrices & Database Searching

[l] ch 3, R2, R3

[l] ch 3, R1, R2 [l] Lecture 2

[l] Protein Structure Prediction [l] ch 8

[l] Structure Alignment [l] ch 8

[l] Gene Expression Analysis [l] ch 12

[l] Clustering [l] ch 10

[l] PCA/SVD [l] ch 11

[l] Gene expression clustering [l] ch 12

[l] Network Biology [l] ch 13-14

[l] Network Centrality and Motifs [l] ch 13

[l] Network Clustering [l] ch 10

[l] Cellular Pathways [l] ch 14

[l] Gene Networks [l] ch 15

[l] Profiles [l] ch 4

[l] Motif Discovery [l] ch 5

[l] Motif Discovery [l] ch 5

[l] Hidden Markov Models [l] ch 6

[l] Hidden Markov Models [l] ch 6

[l] Classification: SVMs [l] ch 7

[l] Sequence Kernels [l] ch 7

[!c] Day: Date [!c] Topic [!c] Reading [!c] Lecture Notes

[l] Overview [l] ch 1-2, R1

[l] Sequence Alignment [l] ch 3 [l]

[l] Scoring Matrices [l] ch 3

[l] Database Searching [l] ch 3

[l] Multiple Sequence Alignment [l] ch 4

• Jan 25: Readings from the book are indicated with each topic in the calendar below.

[l] Molecular Biology Overview

[l] Network Biology (ch 13-14)

[l] Network Centrality and Motifs (ch 13)

[l] Network Clustering (ch 10)

[l] Cellular Pathways (ch 14)

[l] Gene Networks (ch 15)

[l] PCA/SVD (ch 11)

[l] Gene expression clustering (ch 12)

[l] Gene Expression Analysis (ch 12)

[l] Clustering (ch 10)

[l] Gene expression clustering (ch 12)

[l] Protein Structure Prediction (ch 8)

[l] Structure Alignment (ch 8)

[l] EXAM I

[l] Sequence Indexing (Suffix Trees)

[l] Sequence Indexing (Suffix Arrays)

[l] Classification: SVMs (ch 7) [l]

[l] Sequence Kernels (ch 7)

[l]

[l]

[l] Sequence Indexing (Suffix Trees)

[l] Sequence Indexing (Suffix Arrays)

[l] Hidden Markov Models (ch 6)

[l] Hidden Markov Models (ch 6)

[l]S

[l]

[l] Motif Discovery (ch 5)

[l] Motif Discovery (ch 5)

[l] Overview (ch 1-2)

[l] Sequence Alignment (ch 3)

[l] Scoring Matrices (ch 3)

[l] Database Searching (ch 3)

[l] Multiple Sequence Alignment (ch 4)

[l] Profiles (ch 4)

[l]

[l] Sequence Alignment

[l] Scoring Matrices

[l] Database Searching

[l]

[l]

[l] NO CLASS (reading day)

[l]

[l]

[l] NO CLASS (president's day) [row bgcolor=aliceblue] [l]R: Feb 24

[l]

[l] Hidden Markov Models

[l] NO CLASS (spring break)

[l] NO CLASS (spring break)

[l]

[l] [l]

[l] [l]

[l] [l]

[l]

[l]

[l]

[l]

[l]

[l]

[l]

[l]

[l]

[l]

[l]

[l]

[l]

[l]

[l]

[l] Overview

[l]

[l] [l]

[l]

[l] [

[l]

(:tableend:)

[l]M: Jan 24

[l]R: Jan 27

[l]M: Jan 31

[l]R: Feb 3

[l]M: Feb 7

[l]R: Feb 10

[l]M: Feb 14

[l]R: Feb 17

[l]M: Feb 21

[l]R: Feb 24

[l]M: Feb 28

[l]R: Mar 3

[l]M: Mar 7

[l]R: Mar 10

[l]M: Mar 14

[l]R: Mar 17

[l]M: Mar 21

[l]R: Mar 24

[l]M: Mar 28

[l]R: Mar 31

[l]M: Apr 4

[l]R: Apr 7

[l]M: Apr 11

[l]R: Apr 14

[l]M: Apr 18

[l]R: Apr 21

[l]M: Apr 25

[l]R: Apr 28

[l]M: May 2

[l]R: May 5

[l]M: May 9

[l]R: May 12

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.

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.

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 Python, Perl, or R. Only these three scripting languages will be permitted for the assignments.

The required text for the course is

• 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:

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.

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.

CSCI-4964/6964: Computational Biology & Bioinformatics, Spring 2011

Class: 10-11:50AM, MR, Low 4034\\

• Jan 23: Course website is up, with the calendar and syllabus.

[l]Microarray Analysis [l] Lecture 23

[l] Lecture 21

[l]Network Clustering (spectral) [l] Lecture 22

[l]Network Centrality and Motifs [l] Lecture 20

[l]Network Clustering

• Apr 25: Assignment 5 posted.

[l]Network Biology (Graph Models) [l] Lecture 19

[l]Network Clustering

[l] EXAM III

[l]Network Biology [l] Lecture 18

• Apr 13: Assignment 4 posted.

[l] Lecture 17

[l] Lecture 16

[l] Gene Finding

[l]Sequence Assembly [l] Lecture 15

[l]Genome Rearrangement

[l]Microarray Analysis

[l]Microarray Analysis

[l]Network Biology

[l]Network Biology

[l]Network Motifs

[l]Network Clustering

[l]Network Clustering

[l] Lecture 14

[l] Lecture 13

[l] Lecture 12

[l] Lecture 11

[l] Lecture 10

• Mar 17: Assignment 3 posted.
• Mar 15: reading material on HMMs posted.

[l] Lecture 9

[l]Hidden Markov Models

[l]Sequence Indexing (Suffix Trees)

[l]Sequence Indexing (Suffix Arrays)

[l]Sequence Motifs

[l]Sequence Motifs

[l] Phylogenetics (probabilistic) [l] Lecture 8

[l]Sequence Patterns & Indexing (Suffix Trees)

[l] Lecture 7

[l] Phylogenetics (distance-based) [l] Lecture 7

[l] Lecture 6

[l] Phylogenetics (parsimony) [l] [Path:/~zaki/Courses/bioinfo/lectures/Lecture6.pdf|Lecture 6]]

• Feb 23: Readings on multiple sequence alignment and phylogenetics posted.

• Feb 21: Assignment 2 posted.

[l]Multiple Sequence Alignment [l] Lecture 5

[l] Phylogenetics

[l] Phylogenetics

[l]Sequence Patterns & Indexing (Suffix Trees)

[l]Sequence Motifs

[l]Hidden Markov Models

[l]Hidden Markov Models

[l]Structure Alignment

[l] Lecture 4

• Feb 10: Assignment 1 posted.

[l]Sequence Alignment & Scoring [l] Lecture 3

[l]Sequence Scoring & Searching

• 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

[l] Lecture 2

[l] Intro.ppt

[!c]Lectures

[l] Intro.ppt

• 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

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.

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.

• knowledgeable about the fundamental computational biology tasks like sequence and structure analysis and evolution, biological networks, and data mining methods in bioinformatics

• able to implement and apply the techniques to real world omics datasets

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 R or Perl or Python. Only these three scripting languages will be permitted for the assignments, which must be submitted online at the wiki site. Knowledge of pmwiki markup usage will be your responsibility.

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

• 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.

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.

[l]Hidden Markov Models

[l] EXAM I

[l]Phylogenetics

[l]Phylogenetics

[l]Structure Alignment

[l]Structure Prediction

[l]Structure Motifs

[l]Structure Indexing

[l] EXAM II

[l]Network Biology Overview

[l]Network Biology Basics

[l]Network Motifs

[l]Network Clustering

[l]Network Clustering

[l]Microarray Analysis

[l]Microarray Analysis

[l]Kernel Methods in Computational Biology

[l] EXAM III

[l]Kernel Methods

[l] Overview of Biology

[l]Sequence Alignment

[l]Sequence Searching

[l]Sequence Scoring

[l]Sequence Indexing (Suffix Trees)

[l]Sequence Indexing (Suffix Arrays)

[l]Sequence Motifs

[l] EXAM I

[l]Hidden Markov Models

CSCI-4964/6964: Computational Biology, Spring 2010

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] NO CLASS [row bgcolor=aliceblue] [l]R: Jan 28 [l] 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] 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] NO CLASS (spring break) [row bgcolor=aliceblue] [l]R: Mar 11 [l] 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] 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 R software. Students are expected to learn R on their own. Assignments must be submitted online at the wiki site. Knowledge of 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 Rensselaer Handbook of Student Rights and Responsibilities on this topic. (:divend:) (:tableend:)