Visual Exploration of Multidimensional Feature Space of Biological Data

Abstract

Molecular biology is a source of vast quantities of information. Nucleotide sequences, gene expression patterns, protein abundances, sequences and structures, drug activities, gene and metabolic networks are being harvested at laboratories throughout the world. The collected data can be represented by multidimensional feature vectors or by descriptors, which are less formalized, yet still allow one to define similarity relations among objects. Both data representations can be analyzed using data mining and pattern recognition tools. Such tools should allow for interactive, 3-D visual exploration of multidimensional data space by the bio-specialist, rather than for automatic data processing.