Young’s data set

Abraham et al. [2] published a number of papers in which they analyzed Young s data set using MLR and gave a general solvation equation in which various solvent-solute interactions were described by solute descriptors and equation coefficients (Eq. 16)... 

Kaliszan and Markuszewski [23] used the 20-compound Young s data set with slightly different parameters and reported several equations. In Eq. 22, they used the measured log P values given by Young and workers together with the molecular weight ... 

Abraham et al. [2], [3], [4] Young s data set + new compounds (hydrocarbons, gases, and other volatile molecules)... 

Lombardo et al. [29] Basak et al. [5] Kaliszan and Markuszewski [23] Young s data set + Abraham s data set New compounds Young s data set + Abraham s data set... 

Rusinko A III, M W Farmen, C G Lambert, P L Brown and S S Young 1999. Analysis of a Larj Structure/Biological Activity Data Set Using Recursive Partitioning. Journal of Chemic Information and Computer Science 39 1017-1026. 

Assuming that a number of NMR data sets (e.g., 2-D or 3-D maps of displacement vectors resulting from an external periodic excitation) from an object are acquired, the remaining difficulty is their reconstruction into viscoelastic parameters. As written in Section 2 the basic physical equation is a partial differential equation (PDE, Eq. (3)) relating the displacement vector to the density, the attenuation, Young s modulus and Poisson s ratio of the medium. The reconstruction problem is indeed two-fold ... 

Fig. 10. Estimated viscoelatic properties in a normal human breast in vivo. (A) T2 anatomical image. (B) Shear modulus image of the same slice. (Q Young s modulus image of the same slice. Grey scale bars are in kPa. Images B and C are extracted from 3D data sets of reconstructed elasticity parameters, obtained with the subzone based method used in Fig. 8. Note the good contrast in image C, even though the mechanical parameters are not obviously correlated to the structural properties depicted in image A (reprinted with permission from Ref. 48 2000 IOP Publishing Ltd.).

Tropsha, a., and Young, S.S. Automated pharmacophore identification for large chemical data sets. J. Chem. 

Chen, X., Rusinko III, A., Tropsha, A., and Young, S. S. (1999) Automated pharmacophore identification for large chemical data sets. J. Chem. Inf. Comput. Sci. 39, 887-896. 

Hawkins, D. M., Young, S. S., and Rusinko, A. (1997) Analysis of a large structure-activity data set using recursive partitioning. Quantitaive Structure-Activity Relationship 16, 296-302. 

SLoshash You have touched on two different issues. I would leave aside the question of whether transcription is the output. Let s talk about why the numbers are so different. We have thought a lot about this. One big factor is indeed the method of analysis. What we have done is taken our method of analysis. We had access to Mike Young s raw data and we carried out our method of analysis on both data sets. We also got Straume s method of analysis. The different methods of analysis gave completely different results on the same data set. Then we tried one method of analysis on two data sets, but we still got different results. In other words, we still don t know why the conclusions are so different, but it is at least due to two differences between laboratories. 

Young, S.S. and Hawkins, D.M. (1998). Using Recursive Partitioning to Analyze a Large SAR Data Set. SAR QSAR Environ.Res., 8,183-193. 

A. Rusinko III, M.W. Farmen, C.G. Lambert, P.L. Brown, S.S. Young, Analysis of a large structure/biological activity data set using recursive partitioning, J. Chem. Inf. Comput. Sci. 1999, 39, 1017-26. 

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