Information visualization and analytical data mining in pharmaceutical R&D

Area of Science:

• Informatics
• Data Science
• Pharmaceutical Research & Development (R&D)

Background:

• The pharmaceutical industry generates vast amounts of data due to technological advancements in research.
• Traditional data analysis methods struggle to efficiently process and interpret these large-scale, complex datasets.
• There is a growing need for advanced techniques to extract meaningful insights from this data deluge.

Purpose of the Study:

• To review recent literature on data mining in large-scale databases within pharmaceutical R&D.
• To emphasize the synergistic benefits of combining analytical and visual data mining approaches.
• To highlight the increasing adoption of information visualization in pharmaceutical research.

Main Methods:

• Literature review of data mining applications in pharmaceutical R&D.
• Focus on the integration of analytical and visual data mining techniques.
• Analysis of how these methods aid in understanding complex datasets.

Main Results:

• Analytical methods simplify data representations, aiding comprehension.
• Visual data mining complements numerical methods, facilitating understanding at all levels.
• The combined approach is essential for extracting information from large, complex datasets.

Conclusions:

• The integration of analytical and visual data mining is critical for pharmaceutical R&D.
• Information visualization has seen a significant increase in pharmaceutical R&D literature.
• This trend reflects the necessity of advanced data handling techniques for modern research.