• 3 Pages markov
    Markov

    School: Washington

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  • 4 Pages hydro
    Hydro

    School: Washington

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  • 2 Pages scop
    Scop

    School: Washington

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  • 2 Pages blast
    Blast

    School: Washington

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  • 22 Pages genbank
    Genbank

    School: Washington

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  • 4 Pages genbank
    Genbank

    School: Washington

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  • 18 Pages pt-rh
    Pt-rh

    School: Washington

    Finding regulatory modules December 17, 2003 CSE527 Computational Biology Project Presentation Raphael Hoffmann Agenda Importance of module discovery MCAST My Implementation Testing on simulated data Testing on real data Discussion Importance

  • 9 Pages lect16
    Lect16

    School: Washington

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  • 39 Pages lect14
    Lect14

    School: Washington

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  • 111 Pages lect10
    Lect10

    School: Washington

    %!PS-Adobe-2.0 (TeXPS: dvi->PostScript Driver dvitps, Version 3.11 of September 5, 1990\n)print flush %Creator: quinault:ruzzo (Larry Ruzzo,415 Seig,206-543-6298) %DocumentPaperSizes: Letter %Title: Dvi to PostScript converter, Version 3.11 of Septem

  • 141 Pages nov13
    Nov13

    School: Washington

    %!PS-Adobe-3.0 %Title: Microsoft Word - CompBioNotes.doc %Creator: PSCRIPT.DRV Version 4.0 %CreationDate: 12/05/01 11:40:22 %BoundingBox: 19 9 593 784 %Pages: (atend) %PageOrder: Special %Requirements: %DocumentNeededFonts: (atend) %DocumentSuppliedF

  • 3 Pages lect01
    Lect01

    School: Washington

    CSE 527 Computational Biology Autumn 2003 Larry Ruzzo He who asks is a fool for five minutes, but he who does not ask remains a fool forever. - Chinese Proverb Related Courses Genome 540/540 (Winter/Spring) Intro. To Comp. Mol. Bio. Genome 590

  • 935 Pages ppt15
    Ppt15

    School: Washington

    %!PS-Adobe-3.0 %Title: Microsoft PowerPoint - lect15.ppt %Creator: Windows NT 4.0 %CreationDate: 21:30 2/25/1998 %Pages: (atend) %BoundingBox: 26 14 777 596 %Requirements: numcopies(2) collate %LanguageLevel: 2 %DocumentNeededFonts: (atend) %Document

  • 2 Pages notes08
    Notes08

    School: Washington

    CSE 527 notes, 10/22/2007 starting with the slides named "lec04". Slide 22, 24 Notes Ryan Stephens Picked up in the middle of the Expectation-Maximization algorithm. This slide is about the "E-Step". Essentially, what you do is assume that is kno

  • 5 Pages notes02
    Notes02

    School: Washington

    Libby MacKinnon CSE 527 notes October 1, 2007 Intro to Molecular Biology Continued Proteins Proteins are chains of amino acids. They range in length from hundreds to tens of thousands of amino acids. There are 20 different kinds of standard amino ac

  • 3 Pages lec11
    Lec11

    School: Washington

    CSE 527 Lectures 12-13 Markov Models and Hidden Markov Models DNA Methylation CpG - 2 adjacent nts, same strand (not CH3 Watson-Crick pair; p mnemonic for the phosphodiester bond of the DNA backbone) cytosine C of CpG is often (70-80%) methylated

  • 3 Pages lec05
    Lec05

    School: Washington

    Autumn 2007 Lectures 8-9 (& part of 10) Motifs: Representation & Discovery CSE 527 1 DNA Binding Proteins A variety of DNA binding proteins ("transcription factors"; a significant fraction, perhaps 5-10%, of all human proteins) modulate transcript

  • 2 Pages lec04-gmm-em
    Lec04-gmm-em

    School: Washington

    Gaussian Mixture Model via EM A 2component mixture with means mu1 & mu2, both having sigma=1, and mixing parameter tau=0.5. You can CHANGE the DATA, or the INITIAL MUs (yellow boxes), then watch how the E & Msteps update the expected zij's and mus,

  • 244 Pages ppt07
    Ppt07

    School: Washington

    %!PS-Adobe-3.0 %Title: Microsoft PowerPoint - lect07.ppt %Creator: Windows NT 4.0 %CreationDate: 11:19 1/28/1998 %Pages: (atend) %BoundingBox: 15 26 597 777 %LanguageLevel: 2 %DocumentNeededFonts: (atend) %DocumentSuppliedFonts: (atend) %EndComments

  • 1 Page lec04
    Lec04

    School: Washington

    Autumn 2007 Lectures 6-7 MLE, EM, Expression CSE 527 1 Outline MLE: Maximum Likelihood Estimators EM: the Expectation Maximization Algorithm Bio: Gene expression and regulation Next: Motif description & discovery 2 MLE Maximum Likelihood Esti

  • 3 Pages lec01
    Lec01

    School: Washington

    CSE527 Computational Biology http:/www.cs.washington.edu/527 Larry Ruzzo Autumn 2007 UW CSE Computational Biology Group He who asks is a fool for five minutes, but he who does not ask remains a fool forever. - Chinese Proverb Today Admin Why Comp B

  • 2 Pages hw3
    Hw3

    School: Washington

    CSE 527: Computational Biology Assignment #3 Turn this one in on paper; handwritten is fine, I don't recommend trying to typeset it. Extra credit is for extra practice and glory; it is not a big component of your grade. 1. Bayes Rule: In a certain

  • 2 Pages tutorial
    Tutorial

    School: Washington

    CSE 527: Computational Biology March 7, 2000 A review of A Tutorial Introduction to Computation for Biologists, and what else a struggling biologist might need to make sense of CSE 527 When attempting to assign cause to one's confusion, one is immedi

  • 1 Page syl
    Syl

    School: Washington

    CSE 527: Computational Biology, Autumn 2007 An introduction to the use of computational methods for the understanding of biological systems at the molecular level. Intended for graduate students in biological sciences interested in learning about alg

  • 17 Pages hw1
    Hw1

    School: Washington

    %!PS-Adobe-2.0 %Creator: dvips(k) 5.86 Copyright 1999 Radical Eye Software %Title: hw1.dvi %Pages: 3 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %EndComments %DVIPSWebPage: (www.radicaleye.com) %DVIPSCommandLine: /usr/local/bin/dvips -o www/lectures

  • 3 Pages hw1
    Hw1

    School: Washington

    suq ud j u p s i p u p q y w u ewex' eCigwu 'vp}wuvs'{vjsBggCr4vc'zxw u du p i s w u y w w q i yqj dq su y w wj w Cvp}g$'C'vvpX|vsg4Cx"xyv$'vCC|g' re{CxvhvlrqRxyv$q i yqj d dq umw y y dq w dw suju udw s wj p q y w Cv$C

  • 15 Pages lect17
    Lect17

    School: Washington

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  • 1 Page lect16
    Lect16

    School: Washington

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  • 4 Pages lect16
    Lect16

    School: Washington

    Lecture 16 RNA Secondary Structure Prediction February 29, 2000 Notes: Matthew Cary 16.1. RNA Secondary Structure Recall from Section 1.3 that RNA is usually single-stranded in its "normal" state, and this strand folds into a functional shape by fo

  • 4 Pages notes19
    Notes19

    School: Washington

    CSE 527 Notes: Lecture 19, December 1, 2004 Robyn Greaby Covariance Models Cont. Alignment Verterbi: - Inside Algorithm analogous to Forward algorithm for HMMs - Inside: find the most probable sequence of transitions and emissions to produce the seq

  • 12 Pages lect15
    Lect15

    School: Washington

    %!PS-Adobe-2.0 %Creator: dvips(k) 5.86 Copyright 1999 Radical Eye Software %Title: lect15.dvi %Pages: 4 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %DocumentFonts: Times-Bold Times-Roman Times-Italic Courier %EndComments %DVIPSWebPage: (www.radicale

  • 29 Pages lect14
    Lect14

    School: Washington

    %!PS-Adobe-2.0 %Creator: dvips(k) 5.86 Copyright 1999 Radical Eye Software %Title: lect14.dvi %Pages: 4 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %DocumentFonts: Times-Bold Times-Roman Times-Italic %EndComments %DVIPSWebPage: (www.radicaleye.com)

  • 3 Pages notes15
    Notes15

    School: Washington

    Eithon Cadag Lecture 15, 11/17/04 CSE527 Notes on GenScan Generalized HMM - transition probabilities based on sequences, not single nucleotides P(sequence | model) Length distribution introns - geometric (self-loop) terminal exons - modeled empi

  • 3 Pages notes07
    Notes07

    School: Washington

    Computational Biology CSE 527 Autumn 2004 Notes on Lecture 7, October 20th Mathias Ganter mganter@u.washington.edu 24th November 2004 Relative Entropy Relative entropy, also known as the Kullback-Leibler distance or K-L divergence, between two proba

  • 3 Pages lect13
    Lect13

    School: Washington

    Lecture 13 Markov Chains February 17, 2000 Notes: Jonathan Schaefer In Lecture 11 we discovered that correlations between sequence positions are significant, and should often be taken into account. In particular, in Section 11.4 we noted that codons

  • 5 Pages notes04
    Notes04

    School: Washington

    CSE 527: Computational Biology Fall 2004, Professor Larry Ruzzo Notes by Angela Collie, 10/11 Lecture 4: Clustering Expression Data I. Overview: Clustering Expression Data Definition: Clustering is a data exploratory tool. It is an unsupervised appr

  • 5 Pages notes18
    Notes18

    School: Washington

    CSE 527 Lecture Notes Lecture 18, Nov. 26, 2003 Martha Mercaldi - mercaldi@cs Outline 1. Other analyses of HMMs 2. We've assumed we know the model, but how do you generate one? (a) Structure (b) Training (learn parameters given a structure) Hidden M

  • 270 Pages lect10
    Lect10

    School: Washington

    %!PS-Adobe-2.0 %Creator: dvips(k) 5.86 Copyright 1999 Radical Eye Software %Title: lect10.dvi %Pages: 4 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %DocumentFonts: Times-Bold Times-Roman Times-Italic %EndComments %DVIPSWebPage: (www.radicaleye.com)

  • 3 Pages notes19
    Notes19

    School: Washington

    CSE 527 11/29/06 Lecture 19 Larry Jean leijean@amath.washington.edu Paper: Distance vs Accuracy low distance low accuracy greater distance greater accuracy increase distance more decrease accuracy, levels out structure aligning adhoc, useful

  • 16 Pages lect08
    Lect08

    School: Washington

    %!PS-Adobe-2.0 %Creator: dvips(k) 5.86 Copyright 1999 Radical Eye Software %Title: lect08.dvi %Pages: 5 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %DocumentFonts: Times-Bold Times-Roman Times-Italic %EndComments %DVIPSWebPage: (www.radicaleye.com)

  • 5 Pages lect08
    Lect08

    School: Washington

    Lecture 8 Relative Entropy January 27, 2000 Notes: Anne-Louise Leutenegger 8.1. Weight Matrices A weight matrix is any matrix that assigns a score to each sequence according to the formula . The log likelihood ratio matrix described at the end of S

  • 3 Pages notes09
    Notes09

    School: Washington

    Model-based clustering Raphael Hoffmann CSE 527 Lecture Notes, 10/27/03 1 General Idea We assume that each cluster is generated by a multivariate normal distribution. Thus, our total dataset can be regarded as a mixture of different distributions.

  • 4 Pages notes08
    Notes08

    School: Washington

    Nils Gehlenborg (gehlenbo@u.washington.edu) CSE 527 (Fall 2003) - Lecture Notes October 22, 2003. The goal is to introduce the CAST (cluster affinity search technique) clustering algorithm as described in A. Ben-Dor, R. Shamir and Z. Yakhini, "Clus

  • 21 Pages lect06
    Lect06

    School: Washington

    %!PS-Adobe-2.0 %Creator: dvips(k) 5.86 Copyright 1999 Radical Eye Software %Title: lect06.dvi %Pages: 8 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %DocumentFonts: Times-Bold Times-Roman Times-Italic Courier %EndComments %DVIPSWebPage: (www.radicale

  • 3 Pages notes05
    Notes05

    School: Washington

    CSE 527 Lecture Notes - 10/13/03 prepared by: Martha Mercaldi (mercaldi@cs) Clustering Expression Data Why Cluster? Tissue classification Identify biologically related genes (sporulation study) 1st step in understanding regulatory networks (so th

  • 4 Pages notes15
    Notes15

    School: Washington

    CSE 527 Computational Biology Lecture Notes 11/16/05 Kelly Stevens, stevensk@u. Gene Prediction Claverie JM (1997) "Computational methods for the identification of genes." Human Molecular Genetics, 6(10) 1735-1744. This is a good article, but note th

  • 17 Pages lect05
    Lect05

    School: Washington

    %!PS-Adobe-2.0 %Creator: dvips(k) 5.86 Copyright 1999 Radical Eye Software %Title: lect05.dvi %Pages: 6 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %DocumentFonts: Times-Bold Times-Roman Courier Times-Italic %EndComments %DVIPSWebPage: (www.radicale

  • 3 Pages notes04
    Notes04

    School: Washington

    CSE 527 Lecture 4, 10/08/03 Notes by Ana Kristine Torgerson" atorgers@u Case study, continued Sporulation summary What they did Measured mRNA expression levels of all 6200 yeast genes at 7 times points in a (loosely synchronized) sporulating yeast cu

  • 1 Page meme-4up
    Meme-4up

    School: Washington

    Talks this week More Motifs WMM, log odds scores, Neyman-Pearson, background; Greedy & EM for motif discovery Tue, 3:30 EE-105, Me The Search for Non-Coding RNA something similar. Wed, 1:30 K-069, Zasha Weinberg 1 2 Neyman-Pearson Given a sa

  • 33 Pages lect22-splice
    Lect22-splice

    School: Washington

    Pre-mRNA Secondary Structure Prediction Aids Splice Site Recognition Donald J. Patterson, Ken Yasuhara, Walter L. Ruzzo January 3-7, 2002 Pacific Symposium on Biocomputing University of Washington Computational Molecular Biology Group 1 Architectu

  • 1 Page lect04
    Lect04

    School: Washington

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  • 2 Pages notes09
    Notes09

    School: Washington

    Gibbs Sampler Notes Lecture 9, October 28, 2005 Steven Balensiefer How to Average Suppose we have random variables x1 , x2 , . . . , xn with a p.d.f P (x1 , x2 , . . . , xk ) and a particular function f (x1 , . . . , xk ). Now, we want the expected v

  • 1 Page notes04
    Notes04

    School: Washington

    CSE 527 10/9/06 Scribe: David Langworthy (dlan@microsoft.com) -Course Home Page Update change your subscriptions options if you do not use an @U address. Lecture notes and slide numbering will be out of sync. New resource links. -Schedule See new r

  • 3 Pages lect1718
    Lect1718

    School: Washington

    CSE 527 Lecture 17 Markov Models and Hidden Markov Models Viterbi Traceback Above nds probability of best path To nd the path itself, trace backward to state k attaining the max at each stage Lecture 18, 11/26/03 More on HMMs: Viterbi, forward,

  • 3 Pages notes04
    Notes04

    School: Washington

    CSE 527: Computational Biology Fall 2005, Professor Larry Ruzzo Notes by Tom Greene Clustering 101 I. What is it? Group similar objects together Data Exploratory tool: not statistically rigorous, looks for similar structures Why cluster? Tissue cl

  • 3 Pages lect15
    Lect15

    School: Washington

    CSE 527 Lecture 15 More on the Gibbs Sampler Projects Individual or small group Literature: pick 3-5 papers on a coherent topic & give me a report on them, OR implement & test Implementation: 1-2 background papers + Deliverables send me a para

  • 4 Pages lect02
    Lect02

    School: Washington

    Lecture 2 Basics of Molecular Biology (continued) January 6, 2000 Notes: Tory McGrath 2.1. Course Projects A typical course project might be to take some existing biological sequences from the public databases on the web, and design and run some se

  • 3 Pages notes03
    Notes03

    School: Washington

    October 5, 2005 - Lecture #3 Notes by Imran Rashid CSE 527 w/ Prof. Larry Ruzzo HW #1 post review of article by Monday, 10/10 Focused on a review of Chu S, DeRisi J, Eisen M, Mulholland J, Botstein D, Brown PO, Herskowitz I. "The transcriptional pr

  • 3 Pages lec20
    Lec20

    School: Washington

    CSE 527 Phylogeny & RNA: Pfold Lectures 20-21 Autumn 2006 Phylogenies (aka Evolutionary Trees) "Nothing in biology makes sense, except in the light of evolution" - Dobzhansky Modeling Sequence Evolution Simple but useful models; assume: Independen

  • 3 Pages lec17
    Lec17

    School: Washington

    RNA Search and Motif Discovery Lectures 17-19 CSE 527 Autumn 2006 The Human Parts List, circa 2001 1 gagcccggcc 61 gggcgcagcg 121 gcggctcggc 181 tttagcgtcc 241 aaccagagcc 301 caatgtctgt 361 cggaaatcag 421 gccaaatatg 481 acaacactgc 541 ccagaaagga 601

  • 4 Pages cmfinder-4up
    Cmfinder-4up

    School: Washington

    Searching for noncoding RNAs CMfinder - A covariance model based algorithm To appear, Bioinformatics Zizhen Yao Zasha Weinberg Walter L. Ruzzo CM's are great, but where do they come from? A comparative genomic approach Search for motifs with com

  • 3 Pages lect02
    Lect02

    School: Washington

    Lecture 2: DNA Microarray Overview (Some slides from Dr. Holly Dressman, Duke University http:/genome.genetics.duke.edu/STAT_talk_301.ppt) Announcements Go to class web page http:/www.cs.washington.edu/527 Add yourself to class list Check out HW

  • 3 Pages cmfinder
    Cmfinder

    School: Washington

    CMfinder - A covariance model based algorithm To appear, Bioinformatics Zizhen Yao Zasha Weinberg Walter L. Ruzzo University of Washington, Seattle 12/8/05 Searching for noncoding RNAs CM's are great, but where do they come from? A comparative ge

  • 3 Pages lect01
    Lect01

    School: Washington

    CSE 527 Computational Biology http:/www.cs.washington.edu/527 Lecture 1: Overview & Bio Review Autumn 2004 Larry Ruzzo He who asks is a fool for five minutes, but he who does not ask remains a fool forever. - Chinese Proverb Related Courses Genome

  • 3 Pages lec10
    Lec10

    School: Washington

    CSE 527 Lecture 10 Parsimony and Phylogenetic Footprinting (aka Evolutionary Trees) "Nothing in biology makes sense, except in the light of evolution" - Dobzhansky Phylogenies A Complex Question: Given data (sequences, anatomy, .) infer the phylo

  • 4 Pages dec11
    Dec11

    School: Washington

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  • 3 Pages lec04-mle-em-notes
    Lec04-mle-em-notes

    School: Washington

    CSE 527 Lecture Notes: MLE & EM 1 CSE 527, Additional notes on MLE & EM Based on earlier notes by C. Grant & M. Narasimhan Introduction Last lecture we began an examination of model based clustering. This lecture will be the technical background le

  • 308 Pages roottr
    Roottr

    School: Washington

    Lecture Notes on Biological Sequence Analysis 1 Martin Tompa Technical Report #2000-06-01 Winter 2000 Department of Computer Science and Engineering University of Washington Box 352350 Seattle, Washington, U.S.A. 98195-2350 This material is based

  • 2 Pages lec04-gmm-em
    Lec04-gmm-em

    School: Washington

    Gaussian Mixture Model via EM A 2component mixture with means mu1 & mu2, both having sigma=1, and mixing parameter tau=0.5. You can CHANGE the DATA, or the INITIAL MUs (yellow boxes), then watch how the E & Msteps update the expected zij's and mus,

  • 1 Page lec04
    Lec04

    School: Washington

    Autumn 2006 Lectures 6-7 MLE, EM, Expression CSE 527 1 Outline MLE: Maximum Likelihood Estimators EM: the Expectation Maximization Algorithm Bio: Gene expression and regulation Next: Motif description & discovery 2 MLE Maximum Likelihood Esti

  • 3 Pages dec04
    Dec04

    School: Washington

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  • 3 Pages fom
    Fom

    School: Washington

    Cluster Validation for Gene Expression Data Ka Yee Yeung 1 David R. Haynor 2 Walter L. Ruzzo 1 Eisen's Cluster Software (PNAS 1998) Centroid-link hierarchical clustering algorithm Reorder for display Decide on your own cluster! Genes Why Validat

  • 3 Pages lec03
    Lec03

    School: Washington

    CSE 527 Computational Biology Autumn 2006 Lectures 4-5: BLAST Alignment score significance PCR and DNA sequencing 1 This Weeks Plan BLAST Scoring Weekly Bio Interlude: PCR & Sequencing 2 A Protein Structure 3 Topoisomerase I 4 http:/www.rcs

  • 3 Pages lec01
    Lec01

    School: Washington

    CSE527 Computational Biology http:/www.cs.washington.edu/527 Larry Ruzzo Autumn 2006 UW CSE Computational Biology Group He who asks is a fool for five minutes, but he who does not ask remains a fool forever. - Chinese Proverb Today Admin Why Comp

  • 3 Pages lect02
    Lect02

    School: Washington

    Talks today Dr. Phil Green , Professor of Genome Sciences, University of Washington "Finishing the Gene-ome: Computationally Directed Gene Structure Verification in C. elegans" 1:30 in Health Sciences K-069 Dr. Mark Chee , Vice President of Genomic

  • 2 Pages hw3
    Hw3

    School: Washington

    CSE 527: Computational Biology Assignment #3 Turn this one in on paper; handwritten is fine, I don't recommend trying to typeset it. Extra credit is for extra practice and glory; it is not a big component of your grade. 1. Bayes Rule: In a certain

  • 3 Pages lect11
    Lect11

    School: Washington

    CSE 527 Lecture 11 Parsimony and Phylogenetic Footprinting (aka Evolutionary Trees) Nothing in biology makes sense, except in the light of evolution - Dobzhansky Phylogenies A Complex Question: Given data (sequences, anatomy, .) infer the phyloge

  • 1 Page syl
    Syl

    School: Washington

    CSE 527: Computational Biology, Autumn 2006 An introduction to the use of computational methods for the understanding of biological systems at the molecular level. Intended for graduate students in biological sciences interested in learning about alg

  • 5 Pages gibbs-4up
    Gibbs-4up

    School: Washington

    The "Gibbs Sampler" CSE 527 Lecture 9 The Gibbs Sampler Lawrence, et al. "Detecting Subtle Sequence Signals: A Gibbs Sampling Strategy for Multiple Sequence Alignment," Science 1993 The Double Helix Some DNA Binding Domains Los Alamos Science

  • 3 Pages meme
    Meme

    School: Washington

    More Motifs WMM, log odds scores, Neyman-Pearson, background; Greedy & EM for motif discovery Neyman-Pearson Given a sample x , x , ., x , from a 1 2 n distribution f(.|) with parameter , want to test hypothesis = 1 vs = 2. Might as well look

  • 139 Pages lect16
    Lect16

    School: Washington

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  • 2282 Pages ppt16
    Ppt16

    School: Washington

    %!PS-Adobe-3.0 %Title: Microsoft PowerPoint - lect16.ppt %Creator: Windows NT 4.0 %CreationDate: 19:11 3/2/1998 %Pages: (atend) %BoundingBox: 26 14 777 596 %LanguageLevel: 2 %DocumentNeededFonts: (atend) %DocumentSuppliedFonts: (atend) %EndComments %

  • 6 Pages lect03chu-4up
    Lect03chu-4up

    School: Washington

    A Case Study - Chu et al. An interesting early microarray paper My goals The Transcriptional Program of Sporulation in Budding Yeast S. Chu, * J. DeRisi, * M. Eisen, J. Mulholland, D. Botstein, P. O. Brown, I. Herskowitz Science, 282 (Oct 1998

  • 3 Pages lect03chu
    Lect03chu

    School: Washington

    A Case Study - Chu et al. An interesting early microarray paper My goals Show arrays used in a real experiment Show where computation is important Start looking at analysis techniques 1 The Transcriptional Program of Sporulation in Budding Y

  • 3 Pages lect01
    Lect01

    School: Washington

    CSE 527 Computational Biology http:/www.cs.washington.edu/527 Lecture 1: Overview & Bio Review Autumn 2005 Larry Ruzzo He who asks is a fool for five minutes, but he who does not ask remains a fool forever. - Chinese Proverb Related Courses Genome

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    Syl

    School: Washington

    CSE 527: Computational Biology, Autumn 2005 An introduction to the use of computational methods for the understanding of biological systems at the molecular level. Intended for graduate students in biological sciences interested in learning about alg

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