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Computational method for discovery of biomarker signatures from large, complex data sets.
Vladimir Makarov1, Alex Gorlin2
1California State University Channel Islands, Camarillo, CA, 93012, United States; IFXworks LLC, 2915 Columbia Pike, Arlingtion, VA, 22204, United States.
Computational Biology and Chemistry
|July 21, 2018
Summary
This study introduces an efficient computational method to identify reliable biomarker panels from complex biological data. The approach accurately recognizes toxicant exposure conditions, aiding in the discovery of diagnostic biomarkers for toxicity and medical applications.
Area of Science:
- Toxicogenomics
- Computational Biology
- Biomarker Discovery
Background:
- Multivariate data from RNA, small molecule, or protein abundance studies are common in toxicogenomics.
- Identifying reliable biomarker panels from large datasets is challenging.
- Existing methods may not fully leverage comprehensive exposure data.
Purpose of the Study:
- To develop and validate an efficient computational method for identifying biomarker panels.
- To accurately recognize experimental conditions based on toxicant, dose, and time.
- To improve the accuracy of biomarker discovery by incorporating multi-organ and time-response data.
Main Methods:
- Developed a computational methodology for biomarker panel identification.
- Validated the method on the toxicogenomics database Drug Matrix.
- Incorporated gene expression data from multiple organs and time-response information.
Main Results:
- The method successfully identified individual experimental conditions and extrapolated to similar conditions.
- Inclusion of multi-organ gene expression improved recognition accuracy.
- Time-response data enabled highly accurate marker panels, recognizing 176 out of 316 compounds with >90% accuracy.
Conclusions:
- The presented methodology offers an efficient way to discover reliable biomarker panels.
- This approach has direct applications in identifying diagnostic biomarkers for toxicity hazards.
- The methodology may also be valuable for developing biological markers in medical applications.
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