Abstract: Expression data analysis is based mostly on the
statistical approaches that are indispensable for the study of
biological systems. Large amounts of multidimensional data resulting
from the high-throughput technologies are not completely served by
biostatistical techniques and are usually complemented with visual,
knowledge discovery and other computational tools. In many cases,
in biological systems we only speculate on the processes that are
causing the changes, and it is the visual explorative analysis of data
during which a hypothesis is formed. We would like to show the
usability of multidimensional visualization tools and promote their
use in life sciences. We survey and show some of the
multidimensional visualization tools in the process of data
exploration, such as parallel coordinates and radviz and we extend
them by combining them with the self-organizing map algorithm. We
use a time course data set of transitional cell carcinoma of the bladder
in our examples. Analysis of data with these tools has the potential to
uncover additional relationships and non-trivial structures.
Abstract: This paper describes the optimization of a complex
dairy farm simulation model using two quite different methods of
optimization, the Genetic algorithm (GA) and the Lipschitz
Branch-and-Bound (LBB) algorithm. These techniques have been
used to improve an agricultural system model developed by Dexcel
Limited, New Zealand, which describes a detailed representation of
pastoral dairying scenarios and contains an 8-dimensional parameter
space. The model incorporates the sub-models of pasture growth and
animal metabolism, which are themselves complex in many cases.
Each evaluation of the objective function, a composite 'Farm
Performance Index (FPI)', requires simulation of at least a one-year
period of farm operation with a daily time-step, and is therefore
computationally expensive. The problem of visualization of the
objective function (response surface) in high-dimensional spaces is
also considered in the context of the farm optimization problem.
Adaptations of the sammon mapping and parallel coordinates
visualization are described which help visualize some important
properties of the model-s output topography. From this study, it is
found that GA requires fewer function evaluations in optimization
than the LBB algorithm.