'Expression profiles' presentation slideshows

Diagnosis using computers

Diagnosis using computers. One disease. Three therapies. Clinical Studies. In average. 75%. 55%. 35%. Success. Three subtypes of the disease. A. B. C. A. B. C. 100%. 60%. 65%. 40%. 40%. 85%. 10%. 90%. 5%. 100%. A. 91,7%. 90%. B. 85%. C.

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Gene Network Modeling

Gene Network Modeling. Prof. Yasser Kadah Eng. Fadhl Al-Akwaa. OUTLINES. What is the Gene Regulatory Network? GRN Application of GRN GRN Construction Methodology GRN modeling steps GRN Models GRS Software Next work Reference. From The Last Lecture. DNA sequence {A,T,C,G} ATCGAATCGA

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4. Gene Expression Data Analysis

4. Gene Expression Data Analysis. EECS 600: Systems Biology & Bioinformatics Instructor: Mehmet Koyuturk. Analyzing Gene Expression Data. Clustering How are genes related in terms of their expression under different conditions? Differential gene expression

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Overview: Functional Genomics Dissections of Transcriptional Networks

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Engineering Gene Networks: Integrating Synthetic Biology & Systems Biology

Engineering Gene Networks: Integrating Synthetic Biology & Systems Biology. James J. Collins Center for BioDynamics and Department of Biomedical Engineering Boston University. Human Balance Control and Vibrating Insoles. Directed Evolution of Academic Interests. Charles Cantor

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From motif search to gene expression analysis

From motif search to gene expression analysis. Protein Motifs. Protein motifs are usually 6-20 amino acids long and can be represented as a consensus/profile:. P[ED]XK[RW][RK]X[ED]. or as PWM. Protein Domains. In additional to protein short motifs, proteins are characterized by Domains.

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Outline Biological motivation Introduction to graph models and Bayesian network Case study

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Searching for Periodic Gene Expression Patterns Using Lomb-Scargle Periodograms

Searching for Periodic Gene Expression Patterns Using Lomb-Scargle Periodograms. http://research.stowers-institute.org/efg/2004/CAMDA Critical Assessment of Microarray Data Analysis Conference November 11, 2004. Searching for Periodic Gene Expression Patterns Using Lomb-Scargle Periodograms.

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Methods for Micro-Array Analysis Data Mining & Machine Learning Approaches

Methods for Micro-Array Analysis Data Mining & Machine Learning Approaches. What is Micro-Array Analysis?. Analysis of Micro-Array Data. Challenges posed Typical characteristic of micro array data is the large number of variables relative to the number of observations .

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Bi-correlation clustering algorithm for determining a set of co-regulated genes

Bi-correlation clustering algorithm for determining a set of co-regulated genes. BIOINFORMATICS vol. 25 no.21 2009 Anindya Bhattacharya and Rajat K. De. Outline. Introduction Bi-correlation clustering algorithm (BCCA) Results Conclusion. Introduction. Biclustering

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The Role of Preclinical Models to Identify Novel Therapeutics in Rare Cancers

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Environmentally induced foregut remodeling by PHA-4/FoxA and DaF-12/NHR

Environmentally induced foregut remodeling by PHA-4/FoxA and DaF-12/NHR. 1Wanyuan Ao, 1Jeb Gaudet, 2W. james Kent, 1Srikanth Muttumu, 1Susan E. Mango 1Huntsman cancer institute, University of Utah 2Genome bioinformatics Group, University of California. Pharyngeal devleopment depends on pha-4.

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Test system for systems biology

Test system for systems biology. Four distinct types of global datasets were generated and analyzed Proteome analysis quantitative proteomics (ICAT technology) to analyze 300 proteins in wild-type yeast with the system turned on and off

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Introduction to Bioinformatics: Lecture I An Overview of the Course

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Pathways Analysis using Protein Expression Data

Pathways Analysis using Protein Expression Data . Venkatesh Jitender (vjk@cbmi.upmc.edu) Dr. Vanathi Gopalakrishnan Center for Biomedical Informatics, UPMC. Bio-Chemical Pathways & Protein Expression Profiles.

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The Model Experiment

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Diagnostic ALL BM Samples (n = 327)

Diagnostic ALL BM Samples (n = 327). Genes for class distinction (n=271). E2A-PBX1. MLL. T-ALL. Hyperdiploid > 50. BCR-ABL. Novel. TEL-AML1. -3 ?. -2 ?. -1 ?. 0. 1 ?. 2 ?. 3 ?. ALL is a highly heterogeneous disease. T-ALL. Hyperdiploid>50. E2A-PBX1. TEL-AML1. A.

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Inferring microRNA function and evolution from thousands of mRNA expression profiles

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Gene signatures of pulmonary renal cell carcinoma (RCC) metastases

Gene signatures of pulmonary renal cell carcinoma (RCC) metastases predict metastases-free interval and number of metastases per patient. D. Wuttig 1 , M. Meinhardt 2 , S. Zastrow 1 , C. Hoefling 1 , B. Baier 3 , S. Fuessel 1 , M.-O. Grimm 1 , A. Meye 1 , A. Rolle 3 and M.P. Wirth 1

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GAP

Houston et al., 2009 Figure S2. (Glycolysis). (Glycolysis Bypass). GAP. GAP. (12). NAD. GAPC. 5.56. NADH. NADP + H 2 O. (1). NP-GAPDH. 1,3-bPGA. 0.09. (4). NADPH + 2H +. ADP. PGK. 0.96. ATP. 3-PGA. 3-PGA.

177 views • 1 slides