'Microarray gene expression data' presentation slideshows

Classification of Microarray Gene Expression Data

Classification of Microarray Gene Expression Data. Geoff McLachlan Department of Mathematics & Institute for Molecular Bioscience University of Queensland. Institute for Molecular Bioscience, University of Queensland.

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MICROARRAY DATA

1.23k views • 103 slides

Novel representations and methods in text classification

1k views • 91 slides

Extended Overview of Weighted Gene Co-Expression Network Analysis (WGCNA)

Extended Overview of Weighted Gene Co-Expression Network Analysis (WGCNA). Steve Horvath University of California, Los Angeles. Book on weighted networks. E-book is often freely accessible if the library has a subscription to Springer books. Contents.

1.48k views • 73 slides

602 views • 35 slides

Presented by: Renikko Alleyne

A Comprehensive Evaluation of Multicategory Classification Methods for Microarray Gene Expression Cancer Diagnosis. Presented by: Renikko Alleyne. Outline. Motivation Major Concerns Methods SVMs Non-SVMs Ensemble Classification Datasets Experimental Design Gene Selection

519 views • 34 slides

Microarray Analysis Software

Microarray Analysis Software. Maximiliano Corredor Institute of Biology, Leiden University. Steps of a Microarray Experiment. Genomic sequence / EST library sequence. RNA. RT. Annotation. cDNA. labeling. cDNA-Cy3 / -Cy5. Probe design. hybridization. Image Processing. Statistical

444 views • 32 slides

Data mining is the process of automatically extracting valid, novel, potentially useful and ultimately comprehensible in

Data mining is the process of automatically extracting valid, novel, potentially useful and ultimately comprehensible information from very large databases The Data Mining Process data prospecting and surveying transformed data preprocess & transform database selected data make model

609 views • 30 slides

Data mining is the process of automatically extracting valid, novel, potentially useful and ultimately comprehensible in

Data mining is the process of automatically extracting valid, novel, potentially useful and ultimately comprehensible information from very large databases. The Data Mining Process. data prospecting and surveying. transformed data. preprocess & transform. database. selected data.

398 views • 30 slides

Soft Computing & Computational Intelligence

Soft Computing & Computational Intelligence. Biologically inspired computing models Compatible with human expertise/reasoning Intensive numerical computations Data and goal driven Model-free learning Fault tolerant Real world/novel applications. Soft Computing &

452 views • 27 slides

- Especially on Cancer type classification 2003/12/17 Dept. of BioSystem Sangwoo Kim

Uses of SVM and other Algorithms in Analyzing Microarray Gene Expression Data. - Especially on Cancer type classification 2003/12/17 Dept. of BioSystem Sangwoo Kim. Presentation Outline. Introduction Microarray Gene Expression Data Cancer type diagnosis problem

378 views • 27 slides

Introduction to SVM ( Support V ector M achine ) and CRF (C onditional R andom F ield )

Introduction to SVM ( Support V ector M achine ) and CRF (C onditional R andom F ield ). MIS510 Spring 2009. Outline. SVM What is SVM? How does SVM Work? SVM Applications SVM Software/Tools CRF What is SVM? CRF Applications CRF Software/Tools.

2.34k views • 25 slides

Gene regulation and metabolic flux reorganization in aerobic/anaerobic switch of E. coli

Gene regulation and metabolic flux reorganization in aerobic/anaerobic switch of E. coli. Chao WANG July 19, 2006. E. coli is a prokaryote model organism with relatively complete knowledge on both transcriptional regulation (TR) and metabolism.

392 views • 22 slides

Mining Association Rules from Microarray Gene Expression Data

Mining Association Rules from Microarray Gene Expression Data. Association Rules. Form: LHS => RHS , where LHS and RHS are disjoint itemsets.

336 views • 22 slides

Bioinformatics lectures at Rice University

Bioinformatics lectures at Rice University. Lecture 5: Mutual information and Maximum Information Coefficient. -- from Science 16 December 2011. How to compute MIC. Equitability and Generality. Example. Example: Yeast Gene expression cell cycle data. MIC can find new relationships.

338 views • 21 slides

CSE 494/598: Numerical Linear Algebra for Data Exploration

CSE 494/598: Numerical Linear Algebra for Data Exploration. Jieping Ye Department of Computer Science and Engineering Arizona State University http://www.public.asu.edu/~jye02. Course Information. Instructor: Dr. Jieping Ye Office: BY 568 Phone: 480-727-7451 Email: jieping.ye@asu.edu

397 views • 14 slides

BAYESMIX: A software program for Bayesian analysis of mixture models with an application to model-based clustering of microarray gene expression data. Toni Reverter ( Tony.Reverter-Gomez@csiro.au ) Keren Byrne, Brian Dalrymple CSIRO Livestock Industries Queensland Bioscience Precinct

266 views • 13 slides

Controlling FDR in Second Stage Analysis

Controlling FDR in Second Stage Analysis . Catherine Tuglus Work with Mark van der Laan UC Berkeley Biostatistics. Outline. What is a Second Stage Analysis Issues with MTP for Secondary Analysis Proposed solution for Marginal FDR controlling procedure Simulations

304 views • 12 slides

Support vector machines for classification

265 views • 12 slides

Abstract

103 views • 1 slides