Analysis of MALDI-TOF Serum Profiles for Biomarker Selection and Sample Classification

Abstract

Mass spectrometric profiles of peptides and proteins obtained by current technologies are characterized by complex spectra, high dimensionality, and substantial noise. These characteristics generate challenges in discovery of proteins and protein-profiles that distinguish disease states, e.g. cancer patients from healthy individuals. A challenging aspect of biomarker discovery in serum is the interference of abundant proteins with identification of disease-related proteins and peptides. We present data processing methods and computational intelligence that combines support vector machines (SVM) with particle swarm optimization (PSO) for biomarker selection from MALDI-TOF spectra of enriched serum. SVM classifiers were built for various combinations of m/z windows guided by the PSO algorithm. The method identified mass points that achieved high classification accuracy in distinguishing cancer patients from non-cancer controls. Based on their frequency of occurrence in multiple runs, six m/z windows were selected as candidate biomarkers. These biomarkers yielded 100% sensitivity and 91% specificity in distinguishing liver cancer patients from healthy individuals in an independent dataset.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2005
Accession Number
ADA468164

Entities

People

  • C. A. Loffredo
  • E. Orvisky
  • G. L. Hortin
  • H. W. Ressom
  • M. Abdel-hamid
  • R. S. Varghese
  • Robin I. Goldman
  • S. K. Drake

Organizations

  • Georgetown University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Artificial Intelligence
  • Biological Markers
  • Classification
  • Computational Science
  • Detection
  • Identification
  • Information Science
  • Machine Learning
  • Mass Spectra
  • Mass Spectrometry
  • Particle Swarm Optimization
  • Standards
  • Statistical Algorithms
  • Statistical Analysis
  • Supervised Machine Learning

Readers

  • Analytical Chemistry
  • Prostate Cancer Biology.
  • Speech Processing/Speech Recognition.

Technology Areas

  • AI & ML