title: TOFwave: reproducibility in biomarker discovery from time-of-flight mass spectrometry data.
abstract: Many are the sources of variability that can affect reproducibility of disease biomarkers from time-of-flight (TOF) Mass Spectrometry (MS) data. Here we present TOFwave, a complete software pipeline for TOF-MS biomarker identification, that limits the impact of parameter tuning along the whole chain of preprocessing and model selection modules. Peak profiles are obtained by a preprocessing based on Continuous Wavelet Transform (CWT), coupled with a machine learning protocol aimed at avoiding selection bias effects. Only two parameters (minimum peak width and a signal to noise cutoff) have to be explicitly set. The TOFwave pipeline is built on top of the mlpy Python package. Examples on Matrix-Assisted Laser Desorption and Ionization (MALDI) TOF datasets are presented. Software prototype, datasets and details to replicate results in this paper can be found at http://mlpy.sf.net/tofwave/.
authors.affiliation: Fondazione Bruno Kessler, Trento, Italy. chierici@fbk.eu
authors.forename: Marco
authors.initials: M
authors.lastname: Chierici
authors.forename: Davide
authors.initials: D
authors.lastname: Albanese
authors.forename: Pietro
authors.initials: P
authors.lastname: Franceschi
authors.forename: Cesare
authors.initials: C
authors.lastname: Furlanello
mesh_terms: D013058:Mass Spectrometry / Q000379:methods*; D012984:Software; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods