Science & Research

Decision Forest

System Setup Checklist
Installation Instructions
Help Manual
Publications
Contact

Decision Forest is a novel pattern-recognition method which can be used to analyze:

  • DNA microarray data
  • Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry  (SELDI-TOF-MS) data 
  • Structure-Activity Relation (SAR) data
System Setup Checklist

Step 1:  Verify that Java Running Environment (JRE) or Java Development Kit (JDK) 1.4.0 or later is installed on your PC. JRE and JDK may be downloaded from Sun Developer Network (SDN)

Step 2:  Verify that java.exe is on your Path environment variable by following the steps below. If you experience difficulties, your system administrator may be able to help you.  

  • Click "Start" in the lower left-hand corner of your screen and then "Run" in the expanded list. 
  • Type "java" in the new window next to the command prompt and click "OK." 
  • You are ready to use Decision Forest if you see the following text after clicking "OK."     

    Usage:  java [-options] class [args...]
    (to execute a class)
    or java [-options] -jar jarfile [args]
    (to execute a jar file) 

Please seek help from your system administrator if you do not see the text listed above. 

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Installation Instructions

Step 1:  Save the forest.zip file to your local machine. In most browsers you can right-click and choose "Save As" to save the file to your hard drive. 

Step 2:  Unzip forest.zip to any directory. 

Step 3:  Double-click on the file named "run.bat" in the directory where the unzipped files were saved.  This will launch Decision Forest. 

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Help Manual 

The Quick Start Manual for Decision Forest is available for downloading. 

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Publication Year 

  2005 2004 2003  

2005

  • Hong, H., Tong, W., Xie, Q., Fang, H., and Perkins, R. An in silico ensemble method for lead discovery: Decision Forest. SAR and QSAR in Environmental Research. 2005. 16(4):339-347. Abstract
  • Xie, Q., Ratnasinghe, L.D., Hong, H., Perkins, R., Tang, Z.Z., Hu, N., Taylor, P.R., and Tong, W., Decision Rorest analysis of 61 single nucleotide polymorphisms in a case-control study of esophageal cancer; a novel method. BMC Bioinformatics. 2005. 6 Suppl 2:S4. Abstract

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2004

  • Tong, W., Xie, Q., Hong, H., Fang, H., Shi, L., Perkins, R., and Petricoin, E. Using Decision Rorest to classify prostate cancer samples on the basis of SELDI-TOF MS data: assessing chance correlation and prediction confidence. Environmental Health Perspectives. 2004. 112(16):1622-1627. Abstract
  • Hong, H., Tong, W., Perkins, R., Fang, H., Xie, Q., and Shi, L. Multiclass Decision Forest—a novel pattern recognition method for multiclass classification in microarray data analysis. DNA Cell Biology. 2004. 23(10):685-694. Abstract
  • Tong, W., Xie, Q., Hong, H., Fang, H., Shi, L., and Perkins, R., Assessment of prediction confidence and domain extrapolation of two structure-activity relationship models for predicting estrogen receptor binding activity. Environmental Health Perspectives. 2004. 112(12):1249-1254. Abstract

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2003

  • Tong, W., Hong, H., Fang, H., Xie, Q., and Perkins, R. Decision Forest: combining the predictions of multiple independent decision tree models. Jorrnal of Chemical Information and Computer Sciences. 2003. 43(2):525–531. Abstract

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Contact Information 

Please address any questions and suggestions to Dr. Weida Tong at 870-543-7142 or weida.tong@fda.hhs.gov.  

For technical assistance or to report problems with Decision Forest, please send an e-mail to NCTRArrayTrackSupport@nctr.fda.gov


Decision Forest is a product designed and produced by the National Center for Toxicological Research (NCTR).  FDA and NCTR retain ownership of this product.


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Contact FDA

870-543-7000
National Center for Toxicological Research

Food and Drug Administration

3900 NCTR Road

Jefferson, AR 72079

Page Last Updated: 10/14/2014
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