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2015 ; 6
(ä): 458
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Computational Models for Transplant Biomarker Discovery
#MMPMID26441963
Wang A
; Sarwal MM
Front Immunol
2015[]; 6
(ä): 458
PMID26441963
show ga
Translational medicine offers a rich promise for improved diagnostics and drug
discovery for biomedical research in the field of transplantation, where
continued unmet diagnostic and therapeutic needs persist. Current advent of
genomics and proteomics profiling called "omics" provides new resources to
develop novel biomarkers for clinical routine. Establishing such a marker system
heavily depends on appropriate applications of computational algorithms and
software, which are basically based on mathematical theories and models.
Understanding these theories would help to apply appropriate algorithms to ensure
biomarker systems successful. Here, we review the key advances in theories and
mathematical models relevant to transplant biomarker developments. Advantages and
limitations inherent inside these models are discussed. The principles of key
-computational approaches for selecting efficiently the best subset of biomarkers
from high--dimensional omics data are highlighted. Prediction models are also
introduced, and the integration of multi-microarray data is also discussed.
Appreciating these key advances would help to accelerate the development of
clinically reliable biomarker systems.
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