Measuring similarity and improving stability in biomarker identification methods applied to Fourier‐transform infrared (FTIR) spectroscopy

FTIR spectroscopy is a powerful diagnostic tool that can also derive biochemical signatures of a wide range of cellular materials, such as cytology, histology, live cells, and biofluids. However, while classification is a well-established subject, biomarker identification lacks standards and validation of its methods. Validation of biomarker identification methods is difficult because, unlike classification, there is usually no reference biomarker against which to test the biomarkers extracted by a method. In this paper, we propose a framework to assess and improve the stability of biomarkers derived by a method, and to compare biomarkers derived by different method set-ups and between different methods by means of a proposed "biomarkers similarity index".

[1]  Claudia Beleites,et al.  Assessing and improving the stability of chemometric models in small sample size situations , 2008, Analytical and bioanalytical chemistry.

[2]  J. Dwyer,et al.  Biomolecular profiling of metastatic prostate cancer cells in bone marrow tissue using FTIR microspectroscopy: a pilot study , 2007, Analytical and bioanalytical chemistry.

[3]  Michel Manfait,et al.  Differential diagnosis of cutaneous carcinomas by infrared spectral micro-imaging combined with pattern recognition. , 2009, The Analyst.

[4]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[5]  Francis L Martin,et al.  Syrian hamster embryo (SHE) assay (pH 6.7) coupled with infrared spectroscopy and chemometrics towards toxicological assessment. , 2010, The Analyst.

[6]  Barbara H. Stuart,et al.  Infrared Spectroscopy: Fundamentals and Applications: Stuart/Infrared Spectroscopy: Fundamentals and Applications , 2005 .

[7]  Francis L Martin,et al.  Fourier-transform infrared spectroscopy discriminates a spectral signature of endometriosis independent of inter-individual variation. , 2011, The Analyst.

[8]  Galit Shmueli,et al.  Research Commentary - Too Big to Fail: Large Samples and the p-Value Problem , 2013, Inf. Syst. Res..

[9]  D. Naumann FT-INFRARED AND FT-RAMAN SPECTROSCOPY IN BIOMEDICAL RESEARCH , 2001 .

[10]  Ludmila I. Kuncheva,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2004 .

[11]  Jinbo Bi,et al.  Dimensionality Reduction via Sparse Support Vector Machines , 2003, J. Mach. Learn. Res..

[12]  Kevin C. Jones,et al.  Binary mixture effects by PBDE congeners (47, 153, 183, or 209) and PCB congeners (126 or 153) in MCF-7 cells: biochemical alterations assessed by IR spectroscopy and multivariate analysis. , 2010, Environmental science & technology.

[13]  Ernst Wit,et al.  Identifying Variables Responsible for Clustering in Discriminant Analysis of Data from Infrared Microspectroscopy of a Biological Sample , 2007, J. Comput. Biol..

[14]  Peter Lasch,et al.  Detection of preclinical scrapie from serum by infrared spectroscopy and chemometrics , 2007, Analytical and bioanalytical chemistry.

[15]  Erinija Pranckeviciene,et al.  Feature/Model Selection by the Linear Programming SVM Combined with State-of-Art Classifiers: What Can We Learn About the Data , 2007, 2007 International Joint Conference on Neural Networks.

[16]  R. L. Somorjai,et al.  Creating robust, reliable, clinically relevant classifiers from spectroscopic data , 2009, Biophysical Reviews.

[17]  Masoud Nikravesh,et al.  Feature Extraction - Foundations and Applications , 2006, Feature Extraction.

[18]  R L Somorjai,et al.  Near‐optimal region selection for feature space reduction: novel preprocessing methods for classifying MR spectra , 1998, NMR in biomedicine.

[19]  Francis L Martin,et al.  Fourier-transform infrared spectroscopy coupled with a classification machine for the analysis of blood plasma or serum: a novel diagnostic approach for ovarian cancer. , 2013, The Analyst.

[20]  Plamen P. Angelov,et al.  Evolving Fuzzy-Rule-Based Classifiers From Data Streams , 2008, IEEE Transactions on Fuzzy Systems.

[21]  Jeffrey S. Morris,et al.  Quality control and peak finding for proteomics data collected from nipple aspirate fluid by surface-enhanced laser desorption and ionization. , 2003, Clinical chemistry.

[22]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[23]  Francis L Martin,et al.  Distinguishing cell types or populations based on the computational analysis of their infrared spectra , 2010, Nature Protocols.

[24]  L. Mariey,et al.  Discrimination, classification, identification of microorganisms using FTIR spectroscopy and chemometrics , 2001 .

[25]  Felix von Stetten,et al.  Reliable and Rapid Identification of Listeria monocytogenes and Listeria Species by Artificial Neural Network-Based Fourier Transform Infrared Spectroscopy , 2006, Applied and Environmental Microbiology.

[26]  M. Diem,et al.  A decade of vibrational micro-spectroscopy of human cells and tissue (1994-2004). , 2004, The Analyst.

[27]  F. Martin,et al.  Derivation by infrared spectroscopy with multivariate analysis of bimodal contaminant-induced dose-response effects in MCF-7 cells. , 2011, Environmental science & technology.

[28]  Francis L Martin,et al.  Biospectroscopy to metabolically profile biomolecular structure: a multistage approach linking computational analysis with biomarkers. , 2011, Journal of proteome research.

[29]  C LucasHenry,et al.  Research Commentary---Too Big to Fail , 2013 .

[30]  B. Stuart Infrared Spectroscopy , 2004, Analytical Techniques in Forensic Science.

[31]  Francis L Martin,et al.  Diagnostic segregation of human brain tumours using Fourier-transform infrared and/or Raman spectroscopy coupled with discriminant analysis. , 2013, Analytical methods : advancing methods and applications.

[32]  Melanie Hilario,et al.  Stability of feature selection algorithms , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[33]  Plamen P. Angelov,et al.  Robust classification of low-grade cervical cytology following analysis with ATR-FTIR spectroscopy and subsequent application of self-learning classifier eClass , 2010, Analytical and bioanalytical chemistry.

[34]  Thibault Helleputte,et al.  Robust biomarker identification for cancer diagnosis with ensemble feature selection methods , 2010, Bioinform..