Publications [#257800] of Merlise Clyde
Chapters
- Clyde, MA; House, LL; Wolpert, RL. "Nonparametric Models for Proteomic Peak Identification and Quantification." BAYESIAN INFERENCE FOR GENE EXPRESSION AND PROTEOMICS.
Ed. Do, K-A; Müller, P; Vannucci, M Cambridge University Press, 2006: 293-308. [chapter.jsf], [doi]
(last updated on 2026/01/14)Abstract:
We present model-based inference for proteomic peak identification and quantification from mass spectroscopy data, focusing on nonparametric Bayesian models. Using experimental data generated from MALDI-TOF mass spectroscopy (matrix-assisted laser desorption ionization time-of-flight) we model observed intensities in spectra with a hierarchical nonparametric model for expected intensity as a function of time-of-flight. We express the unknown intensity function as a sum of kernel functions, a natural choice of basis functions for modeling spectral peaks. We discuss how to place prior distributions on the unknown functions using Lévy random fields and describe posterior inference via a reversible jump Markov chain Monte Carlo algorithm.