libfbi: a C++ implementation for fast box intersection and application to sparse mass spectrometry data

Abstract. This document is a preprint of the following publication: Bioinformatics (2011) 27(8): 1166-1167. Algorithms for sparse data require fast search and subset selection capabilities for the determination of point neighborhoods. A natural data representation for such cases are space partitioning data structures. However, the associated range queries assume noise-free observations and cannot take into account observation-specific uncertainty estimates that are present in e.g. modern mass spectrometry data. In order to accommodate the inhomogeneous noise characteristics of sparse real-world data sets, point queries need to be reformulated in terms of box intersection queries, where box sizes correspond to uncertainty regions for each observation. This contribution introduces libfbi, a standard C++, header-only template implementation for fast box intersection in an arbitrary number of dimensions, with arbitrary data types in each dimension. The implementation is applied to a data ...

• Type: Journal
• Year: 2011
• Where: BIOINFORMATICS
• Authors: Marc Kirchner, Buote Xu, Hanno Steen, Judith A. J. Steen
• Keywords: BIOINFORMATICS 2011, Biometrics, Liquid Chromatography Mass Spectrometry, Mass Spectrometry Data, Uncertainty Estimates