A comparison using different non-linear projection models has been demonstrated using a single data set projected by using two almost orthogonal lists of 70 genes as feature vectors for each patient.
Prior efforts on this data set are summarized in the Table 2 in Yan and Zheng (2007) [ 9], which shows that this data set projects a difficult task.
This project was done individually and, in order to provide further scaffolding, students were required to submit their data set and project plan for approval before completing the project.
The Minimum Data Set (MDS) project on Ageing – supported by the World Health Organization (WHO) and the U.S. National Institute on Ageing – has set 50 years and above as the cut-off to refer to the older population in Africa (4, 7).
The aim of PCA is to compress the data set by projecting the samples on a low-dimensional subspace without losing the relevant information.
Fig. 3A shows how activity is distributed across the various structural classes when the compounds in the data set are projected into two dimensions using embedded non-linear mapping [40, 41] based on the similarity in their molecular fields: symbols are colored by structural class and sized by activity.
Therefore, PCA will be used as a means of constructing an informative graphical representation of the data set by projecting the data onto a lower dimensional space.
Principal component analysis is very useful to reduce the dimensionality of a data set by projecting high dimensional data into a lower dimensional space.
The second set of models (in vivo disconnects) defined the fractional polynomial powers based on the in vivo data set and projected its dose-response profiles to the in vitro data set.
The first set of models (in vitro disconnects) defined the fractional polynomial powers based on the in vitro data set and projected its dose-response profiles to the in vivo data set.
