Ecotoxicology

, Volume 21, Issue 3, pp 631–636 | Cite as

Finding biomarkers is getting easier

Review Article

Abstract

Single biomarkers are rarely accurate. Even suites of biomarkers can give conflicting results. Ideally potent combinations of variables are isolated which accurately identify specific analytes and their level of toxicity. The search for such combinations can be done by reducing the thousands of candidate variables to the small number necessary for treatment classification. When the key variables are recognized by machine learning (ML) the results are quite surprising, given the apparent failure of other searching methods to produce good diagnostics. Proteins seem especially useful for portable field tests of a variety of adverse conditions. This review shows how ML, in particular artificial neural networks, can find potent biomarkers embedded in any type of expression data, mainly proteins in this article. A computer does multiple iterations to produce sets of proteins which systematically identify (to near 100% accuracy) the treatment classes of interest. Whether these proteins are useful in actual diagnoses is tested by presenting the computer model with unknown classes. Finding the biomarkers is getting easier but there still must be confirmation, by multivariable statistics and with field studies.

Keywords

Isolating biomarkers Neural networks Machine learning Statistical verification 

Notes

Acknowledgments

First to my colleague Mike O’Neill, who suggested ANN some time ago and then fitted the models. Next to all the graduate students who collected the data and the evidence that PES (Protein expression signatures) seemed to exist for everything. To Nagaraj Neerchal and his students, Sverdlov and Siddani for their parametric analysis of the medaka (uv) data. To the questions from attendees at PRIMO and SETAC meetings, the annoying, the amusing and the useful, questions that is. To two reviewers for (somewhat) restraining my verbosity. Not least to Editor-in-Chief Lee Shugart for his patience and his valuable comments. And to Justice Oliver Holmes, Jr., posthumously, for the quote. I am supported currently by TIAA (an NGO).

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Biological SciencesUniversity of Maryland Baltimore CountyBaltimoreUSA
  2. 2.BuffaloUSA

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