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PNAS 100 (16): 9608-9613

Copyright © 2003 by the National Academy of Sciences.

Prediction of clinical drug efficacy by classification of drug-induced genomic expression profiles in vitro

Erik C. Gunther*,{dagger}, David J. Stone*, Robert W. Gerwien, Patricia Bento, and Melvyn P. Heyes

CuraGen Corporation, 322 East Main Street, Branford, CT 06405


Figure 1
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Fig. 1. Three-dimensional representation of class discrimination on the basis of biomarker expression: classification tree. All possible three-way combinations of the four-gene marker set from CT iteration-one are displayed: (CG50207 vs. ENTPD6 vs. PTX3) (A), (CG50207 vs. ILK vs. PTX3) (B), (ILK vs. ENTPD6 vs. PTX3) (C), and (CG50207 vs. ENTPD6 vs. ILK) (D). Axes represent relative expression levels of marker genes, with means set to 1.0. Each graph is shown perpendicular to the XY plane (Left), and from 45° rotation around the y axis (Right). Red, antidepressant; dark blue, antipsychotic; green, opioid; brown, PCP; black, amphetamine; light blue, vehicle control; squares, correctly predicted treatments; triangles, misclassified treatments. Lines connecting correctly predicted treatments delineate the volume occupied by each accurately defined sample class. Note distinct class separation and placement of untreated control samples outside the treatment classes.

 

Figure 2
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Fig. 2. Three-dimensional representation of class discrimination on the basis of biomarker expression: random forest. The three biomarkers with importance measures >0.75 are depicted: (SFRS7 vs. SCG3 vs. CG187232–01). Axes represent relative expression levels of marker genes, with means set to 1.0. The graph is shown perpendicular to the XY plane (Left), and from 45° rotation around the y axis (Right). Red, antidepressant; dark blue, antipsychotic; green, opioid; brown, PCP; black, amphetamine; light blue, vehicle control; squares, correctly predicted treatments; triangles, misclassified treatments.

 


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