'Expression level' diaporamas de présentation

Expression level - PowerPoint PPT Presentation


Original Figures for "Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression M

Original Figures for "Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression M

Original Figures for "Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring".

By jacob
(315 views)

Statistics for Microarrays

Statistics for Microarrays

Statistics for Microarrays. Multiple Testing and Prediction and Variable Selection. Class web site: http://statwww.epfl.ch/davison/teaching/Microarrays/. cDNA gene expression data. mRNA samples. Data on G genes for n samples. sample1 sample2 sample3 sample4 sample5 …

By templeton
(289 views)

Critical Regulation of Thymic Epithelial Cell Function and Thymus Development by Transforming Growth Factor-Beta Signali

Critical Regulation of Thymic Epithelial Cell Function and Thymus Development by Transforming Growth Factor-Beta Signali

Critical Regulation of Thymic Epithelial Cell Function and Thymus Development by Transforming Growth Factor-Beta Signaling. Michael Blazar 2006. Thymus. Blood vessels. Medulla. Cortex. Membrane. http://www.becomehealthynow.com/popups/thymus.htm. Hollander presentation.

By jemima
(225 views)

Gene Expression BMI 731 Winter 2005

Gene Expression BMI 731 Winter 2005

Gene Expression BMI 731 Winter 2005. Catalin Barbacioru Department of Biomedical Informatics Ohio State University. Thesis: the analysis of gene expression data is going to be big in 21st century statistics. Many different technologies, including Spotted DNA arrays (Brown/Botstein)

By dingbang
(214 views)

Lecture 8 Confidence interval

Lecture 8 Confidence interval

Lecture 8 Confidence interval. Parameter and estimate Standard error of the mean (SE) 95% confidence interval Confidence level (coefficient) 1- a Using z score Two sample problem; matched sample problem Illustration with Computer simulation . : population mean, sample mean. Which one? .

By nemo
(190 views)

High-throughput Proteomics

High-throughput Proteomics

High-throughput Proteomics. David Birnbaum. Introduction. What is Proteomics ? Proteomics is the analysis of genomic complements of proteins. Why proteomics ? Until now we have looked at many methods which deal with RNA & DNA. However, Proteins ultimately define how the cell behaves.

By zazu
(325 views)

Principal Component Analysis (PCA) for Clustering Gene Expression Data

Principal Component Analysis (PCA) for Clustering Gene Expression Data

Principal Component Analysis (PCA) for Clustering Gene Expression Data. K. Y. Yeung and W. L. Ruzzo. Organization. Association of PCA and this paper Approach of this paper Data sets Clustering algorithms and similarity metrics Results and discussion. The Functions of PCA?.

By hart
(193 views)

PDK1 nucleates T cell receptor-induced signaling complex for NF-kappaB activation.

PDK1 nucleates T cell receptor-induced signaling complex for NF-kappaB activation.

PDK1 nucleates T cell receptor-induced signaling complex for NF-kappaB activation. Lee KY, et al. Science 308:114-118(2005) Chen-Chung Lin, Dep. of Cell Biology. Introduction (TCR-CD3 complex ). TCR-CD3 complex. (Immunoreceptor tyrosine-based activation motifs). Signaling cascade.

By halen
(190 views)

Discrimination Methods

Discrimination Methods

Discrimination Methods. As Used In Gene Array Analysis. Discrimination Methods. Microarray Background Clustering and Classifiers Discrimination Methods: Nearest Neighbor Classification Trees Maximum Likelihood Discrimination Fisher Linear Discrimination Aggregating Classifiers Results

By giza
(148 views)

Hutchinson-Guilford Progeria

Hutchinson-Guilford Progeria

Hutchinson-Guilford Progeria. premature aging lifespan = 13.4 years retarded growth midface hypoplasia micrognathia alopecia low adiposity osteodysplasia premature, severe atherosclerosis -death due to MI. De Sandre-Ciovannoli, Science express, 17 April 2003. Lamin A mutations in HGS.

By gamba
(344 views)

Timothy H. W. Chan, Calum MacAulay, Wan Lam, Stephen Lam, Kim Lonergan, Steven Jones, Marco Marra, Raymond T. Ng

Timothy H. W. Chan, Calum MacAulay, Wan Lam, Stephen Lam, Kim Lonergan, Steven Jones, Marco Marra, Raymond T. Ng

Pool together cancer and normal libraries. Null Hypothesis: Alternative Hypothesis. Simulated Normal Pool (same size as normal samples). Simulated Cancer Pool (same size as cancer samples ). Mean. Sum of Squares. N. Observed. Simulated . Standard Deviation. PLOT. Score those >=99%

By aubrie
(196 views)

Integration of Expression Data and Genotype Data: Application of Chronic Fatigue Syndrome Data

Integration of Expression Data and Genotype Data: Application of Chronic Fatigue Syndrome Data

Integration of Expression Data and Genotype Data: Application of Chronic Fatigue Syndrome Data. EunJee Lee 1 , Seoae Cho 1 , Taesung Park 2. 1 Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea 2 Department of Statistics, Seoul National University, Seoul, Korea.

By finnea
(2 views)

DNA Chip Data Interpretation Tools: Genmapp & Dragon View

DNA Chip Data Interpretation Tools: Genmapp & Dragon View

DNA Chip Data Interpretation Tools: Genmapp & Dragon View. In-Song Koh, M.D., Ph.D. Genomic Research Center for Lung & Breast/Ovarian Cancers College of Medicine, Korea University. cDNA Microarray Schema Duggan et al., Nature Genetics 1999. 1. Array Fabrication. 2. Probe Preparation

By pancho
(158 views)

Target mRNA abundance dilutes microRNA and siRNA activity

Target mRNA abundance dilutes microRNA and siRNA activity

Target mRNA abundance dilutes microRNA and siRNA activity. Subtitle: All Target Genes Are Sponges. Aaron Arvey Memorial Sloan Kettering Cancer Center MicroRNAs and Human Disease February 12th 2011. Target Concentration. Downregulation.

By westbrook
(98 views)

Targeted therapies in lung cancer - what are the limits?

Targeted therapies in lung cancer - what are the limits?

Targeted therapies in lung cancer - what are the limits?. D. Ross Camidge , MD PhD Director, Thoracic Oncology Clinical Program University of Colorado. Halifax, Nova Scotia, 21 st October 2011. Disclosures (DRC). Employment or leadership Position: None

By kalila
(180 views)

Gene Transfer for Neovascular Age-Related Macular Degeneration

Gene Transfer for Neovascular Age-Related Macular Degeneration

Gene Transfer for Neovascular Age-Related Macular Degeneration. Peter A. Campochiaro The Wilmer Eye Institute The Johns Hopkins University School of Medicine Baltimore, MD. Financial Disclosure. Research Support Genzyme Oxford BioMedica AskBio. Topics. Neovascular AMD Background

By nishi
(125 views)

Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring

Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring

Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring . Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES.

By paley
(220 views)

Gene Expression BMI 731 week 5

Gene Expression BMI 731 week 5

Gene Expression BMI 731 week 5. Catalin Barbacioru Department of Biomedical Informatics Ohio State University. Thesis: the analysis of gene expression data is going to be big in 21st century statistics. Many different technologies, including High-density nylon membrane arrays

By gema
(173 views)

Basic methodologies1

Basic methodologies1

Basic methodologies1. . UNSUPERVISED: EXPLORATORY ANALYSIS. NO PRIOR KNOWLEDGE IS USED EXPLORE STRUCTURE OF DATA ON THE BASIS OF CORRELATIONS AND SIMILARITIES. BASIC METHODOLOGIES OF ANALYSIS:. SUPERVISED ANALYSIS : HYPOTHESIS TESTING.

By lucia
(98 views)

RDA Special Topics: Compilations & Collaborations—Pt. 3 Ana Lupe Crist á n Policy and Standards Division Revised

RDA Special Topics: Compilations & Collaborations—Pt. 3 Ana Lupe Crist á n Policy and Standards Division Revised

RDA Special Topics: Compilations & Collaborations—Pt. 3 Ana Lupe Crist á n Policy and Standards Division Revised July 2012 Adapted for UC San Diego Catalogers Presented August 27, 2013 by Marilú Vallejo and Shi Deng. Constructing AAP for Compilations in four parts.

By kalea
(166 views)

View Expression level PowerPoint (PPT) presentations online in SlideServe. SlideServe has a very huge collection of Expression level PowerPoint presentations. You can view or download Expression level presentations for your school assignment or business presentation. Browse for the presentations on every topic that you want.