GEPAS -Gene Expression Pattern Analysis Suite Hongli Li Computer Science Department UMASS Lowell

Slides:

Advertisements

Similar presentations

Prediction of Sickle Cell Anemia Patient’s Response to Hydroxyurea Treatment Using ARTMAP Network Hongyu Xu, Faramarz Valafar, Marko Vuskovic Department.

Advertisements

NYU Microarray Database (NYUMAD)

Bioinformatics Spring Jianping Zhou Extraction of functional information from large-scale gene expression data.

Microarray GEO – Microarray sets database

April 23, 2001LBSC 878 Text Data Mining Douglas W. Oard.

Yeast Dataset Analysis Hongli Li Final Project Computer Science Department UMASS Lowell.

Automatic Pose Estimation of 3D Facial Models Yi Sun and Lijun Yin Department of Computer Science State University of New York at Binghamton Binghamton,

Department of Computer Science, University of Waikato, New Zealand Eibe Frank WEKA: A Machine Learning Toolkit The Explorer Classification and Regression.

Scientific Data Mining: Emerging Developments and Challenges F. Seillier-Moiseiwitsch Bioinformatics Research Center Department of Mathematics and Statistics.

Demonstration Trupti Joshi Computer Science Department 317 Engineering Building North (O)

Microarray Analysis Software at NIH. BRB ArrayTools Visualization and Statistical analysis of gene expression data Features –Excel Add-in –Flexible Data.

Cleaver – Classification of Expression Array Version 1.0 Hongli Li Spring Computational Biology Computer Science Department UMASS Lowell.

Introduction to WEKA Aaron 2/13/2009. Contents Introduction to weka Download and install weka Basic use of weka Weka API Survey.

GEPAS Gene Expression Pattern Analysis Suite Ka-Lok Ng Dept. of Bioinformatics Asia University.

1 A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions Zhihong Zeng, Maja Pantic, Glenn I. Roisman, Thomas S. Huang Reported.

Tutorial 8 Clustering 1. General Methods –Unsupervised Clustering Hierarchical clustering K-means clustering Expression data –GEO –UCSC –ArrayExpress.

1 An Excel-based Data Mining Tool Chapter The iData Analyzer.

Blink Sakulkueakulsuk. 1. D. Wilking, and T. Rofer, Realtime Object Recognition Using Decision Tree Learning, 2005

Introduction to Data Mining Engineering Group in ACL.

CSCI 347 / CS 4206: Data Mining Module 05: WEKA Topic 04: Data Preparation Tools.

Web Technologies in Bioinformatics T.J. Esposito April 28, 2005 Advanced Bioinformatics Computing.

Lab2 CPIT 440 Data Mining and Warehouse.

Analysis and Management of Microarray Data Dr G. P. S. Raghava.

Generalized Fuzzy Clustering Model with Fuzzy C-Means Hong Jiang Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, US.

Dr Paul Lewis Lecturer in Bioinformatics Lecturer in Bioinformatics Cardiff University Cardiff University Biostatistics & Bioinformatics Unit Biostatistics.

Preprocessing for Data Mining Vikram Pudi IIIT Hyderabad.

Advanced Database Course (ESED5204) Eng. Hanan Alyazji University of Palestine Software Engineering Department.

Analysis and Management of Microarray Data Previous Workshops –Computer Aided Drug Design –Public Domain Resources in Biology –Application of Computer.

Introduction: Olfactory Physiology Organic Chemistry Signal Processing Pattern Recognition Computational Learning Electronic Nose Chemical Sensors.

Data Warehousing Lecture-30 What can Data Mining do? Virtual University of Pakistan Ahsan Abdullah Assoc. Prof. & Head Center for Agro-Informatics Research.

Figure SOM1. Functional roles of the genes affected in zmet2-m1 mutants. Although the genes localized on the intracellular membranes were slightly over-represented.

Gene-Markers Representation for Microarray Data Integration Boston, October 2007 Elena Baralis, Elisa Ficarra, Alessandro Fiori, Enrico Macii Department.

BOĞAZİÇİ UNIVERSITY DEPARTMENT OF MANAGEMENT INFORMATION SYSTEMS MATLAB AS A DATA MINING ENVIRONMENT.

Nuria Lopez-Bigas Methods and tools in functional genomics (microarrays) BCO17.

Kansas State University Department of Computing and Information Sciences CIS 730: Introduction to Artificial Intelligence Friday, 14 November 2003 William.

1 Unsupervised Learning and Clustering Shyh-Kang Jeng Department of Electrical Engineering/ Graduate Institute of Communication/ Graduate Institute of.

Weka Tutorial. WEKA:: Introduction A collection of open source ML algorithms – pre-processing – classifiers – clustering – association rule Created by.

A new clustering tool of Data Mining RAPID MINER.

Tutorial 8 Gene expression analysis 1. How to interpret an expression matrix Expression data DBs - GEO Clustering –Hierarchical clustering –K-means clustering.

Cluster Analysis Data Mining Experiment Department of Computer Science Shenzhen Graduate School Harbin Institute of Technology.

WEKA's Knowledge Flow Interface Data Mining Knowledge Discovery in Databases ELIE TCHEIMEGNI Department of Computer Science Bowie State University, MD.

Cluster Analysis Dr. Bernard Chen Assistant Professor Department of Computer Science University of Central Arkansas.

An Excel-based Data Mining Tool Chapter The iData Analyzer.

Copyright © 2004 by Jinyan Li and Limsoon Wong Rule-Based Data Mining Methods for Classification Problems in Biomedical Domains Jinyan Li Limsoon Wong.

Expression profiling & functional genomics Exercises.

Introduction to Classification & Clustering Villanova University Machine Learning Lab Module 4.

Introduction to Oncomine Xiayu Stacy Huang. Oncomine is a cancer-specific microarray database and has a web-based data-mining platform aimed at facilitating.

Microarray Technology and Data Analysis Roy Williams PhD Sanford | Burnham Medical Research Institute.

Topic 4: Cluster Analysis Analysis of Customer Behavior and Service Modeling.

Cluster Analysis This work is created by Dr. Anamika Bhargava, Ms. Pooja Kaul, Ms. Priti Bali and Ms. Rajnipriya Dhawan and licensed under a Creative Commons.

Machine Learning for Computer Security

Introduction to Classification & Clustering

A Methodology for Finding Bad Data

Introduction to Data Mining

Waikato Environment for Knowledge Analysis

Topic 3: Cluster Analysis

An Excel-based Data Mining Tool

Research Areas Christoph F. Eick

Gene Expression Analysis and Proteins

Prepared by: Mahmoud Rafeek Al-Farra

An Introduction to Supervised Learning

Classification and Prediction

Data Mining, Machine Learning, Data Analysis, etc. scikit-learn

Lecture 10 – Introduction to Weka

Elements of Branding Product Differentiation Relevance Perceived Value.

By Sandeep Patil, Department of Computer Engineering, I²IT

©Jiawei Han and Micheline Kamber

Topic 5: Cluster Analysis

Promising “Newer” Technologies to Cope with the

Tel Hope Foundation’s International Institute of Information Technology, (I²IT). Tel

Presentation transcript:

GEPAS -Gene Expression Pattern Analysis Suite Hongli Li Computer Science Department UMASS Lowell

Features Preprocessing –Log-transformation, replication handling, missing value imputation, filtering and normalization Analysis Tools –Viewer –Unsupervised Clustering –Differential Gene Expression –Supervised Classification –Data Mining with Gene Ontology (GO)

Pre-Analyses & Preprocessing Pre-analyses Preprocessing the data –Transform –Deal with replication and missing value –Filter patterns, standardize patterns –Filter genes

After Preprocess

PlotCorr

SOM

Cluster

EPCLUST Steps –Send to EPCLUST from GEPAS –Proceed –Go to respective page, remember the file name –Select the data interested –Use the interested tools to analysis

Conclusion Easy to use Preprocessing is quite powerful Connected to gene database