Data Mapping

While traditional data modeling techniques are often applied to business problems, many of the innovative models actually originate from academic research. One of the fastest growing areas of scientific modeling is in the field of computational biology, which applies data models to cellular phenomena. Although analysts may not realize it, many of the techniques that are commonly applied today can be traced to this field. In order to better understand some of the forthcoming ideas in the modeling field, we review some of these research areas:

Bioinformatics
A new field within molecular biology, bioinformatics applies databases to help solve modeling problems in biology. Among the most innovative techniques within the field is the development of large scale databases to develop accurate models of protein structures. As a result, the field has led to innovations in data mining and machine learning techniques which have been ported over to business analysis.

Computational Genomics
In the racing to model the human genome, Craig Venter led a private research team which utilized computing power to out-paced government-funded efforts. Since that time, leading scientists have been working on techniques to improve the speed of genetic analysis, which has greatly accelerated many efforts in pharmaceutical research.

Molecular Modeling
A broad field that has helped to provide a better understanding of the behavior of molecular compounds for improvements in material science and chemical research, molecular modeling has been used to help scientists better understand protein folding and enzyme behavior. The field has, therefore, been central in helping to design new, improved materials and drugs, as well as leading to a number of software programs which are now also used in social sciences such as Gaussian.