title: A Bayesian network approach to feature selection in mass spectrometry data.
abstract: BACKGROUND
  Time-of-flight mass spectrometry (TOF-MS) has the potential to provide non-invasive, high-throughput screening for cancers and other serious diseases via detection of protein biomarkers in blood or other accessible biologic samples. Unfortunately, this potential has largely been unrealized to date due to the high variability of measurements, uncertainties in the distribution of proteins in a given population, and the difficulty of extracting repeatable diagnostic markers using current statistical tools. With studies consisting of perhaps only dozens of samples, and possibly hundreds of variables, overfitting is a serious complication. To overcome these difficulties, we have developed a Bayesian inductive method which uses model-independent methods of discovering relationships between spectral features. This method appears to efficiently discover network models which not only identify connections between the disease and key features, but also organizes relationships between features--and furthermore creates a stable classifier that categorizes new data at predicted error rates.
  
  
  RESULTS
  The method was applied to artificial data with known feature relationships and typical TOF-MS variability introduced, and was able to recover those relationships nearly perfectly. It was also applied to blood sera data from a 2004 leukemia study, and showed high stability of selected features under cross-validation. Verification of results using withheld data showed excellent predictive power. The method showed improvement over traditional techniques, and naturally incorporated measurement uncertainties. The relationships discovered between features allowed preliminary identification of a protein biomarker which was consistent with other cancer studies and later verified experimentally.
  
  
  CONCLUSIONS
  This method appears to avoid overfitting in biologic data and produce stable feature sets in a network model. The network structure provides additional information about the relationships among features that is useful to guide further biochemical analysis. In addition, when used to classify new data, these feature sets are far more consistent than those produced by many traditional techniques.
authors.affiliation: Department of Physics, The College of William and Mary, Williamsburg, VA, USA. kwkusc@wm.edu
authors.forename: Karl W
authors.initials: KW
authors.lastname: Kuschner
authors.forename: Dariya I
authors.initials: DI
authors.lastname: Malyarenko
authors.forename: William E
authors.initials: WE
authors.lastname: Cooke
authors.forename: Lisa H
authors.initials: LH
authors.lastname: Cazares
authors.forename: O J
authors.initials: OJ
authors.lastname: Semmes
authors.forename: Eugene R
authors.initials: ER
authors.lastname: Tracy
mesh_terms: D001499:Bayes Theorem; D015415:Biomarkers / Q000737:chemistry; D013058:Mass Spectrometry / Q000379:methods*; D010363:Pattern Recognition, Automated; D040901:Proteomics / Q000379:methods*
chemical_list: D015415:Biomarkers