Genre: Information/Explanation

T.J. Oldfield , L.S.D. Caves , B. Grant , A. Rodrigues , & C. Dieterich . The analysis of large amounts of data generated from protein X-ray crystallography / NMR , as well as genomic sequencing , is a daunting task given the size of the data . We are approaching this problem using mathematical data mining techniques . Data mining is the analysis of a set of data using only mathematical targets ( numerical occurrence , derivatives and 2nd derivatives ) to define a signal ( something interesting in the data that is not noise ) . This has the advantage that any result from the analysis is not biased by any preconceived ideas about protein structure or genome content . Its main disadvantage is that the method does not give the biological reason for a particular result because the target of the analysis is a simple mathematical metric . To date , the analysis of protein data has focused on the understanding of folds within the proteins and local interactions of residues / atoms . The latter identifies ligand-binding sites . The analysis of genomes has focused on the mathematical derivation of information content within genomes / ORFs and has generated some interesting relationships between coding and non-coding regions of genomes . Funding : Accelrys References : T.J. Oldfield " Creating structure features by data mining the PDB to use as Molecular Replacement Models " . Accepted for publication Acta . Cryst . D.