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Decision Forest Home
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          Decision ForestBioinformatics Tools Decision Forest
  			Novel pattern-recognition method which can be used to analyze DNA microarray, SELDI-TOF MS, and SAR data.
	
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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) dataSystem 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.

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.


Help Manual

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

Publication Year

2005
			2004
			20032005

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. Abstract2004

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. Abstract2003

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. AbstractContact 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.*

Resources For You

About the National Center for Toxicological Research
	Decision Forest QuickStart Manual
	Forest.zip
	Mold2
	Bioinformatics Tools
	Get Email Updates for NCTR

                                                                                  Content current as of:
      08/17/2022
                  
                   
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				    				       				    				       Toxicology
				       				    				       BioinformaticsDecision Forest
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