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Computational Models for Transplant Biomarker Discovery.

Wang A, Sarwal MM - Front Immunol (2015)

Bottom Line: Understanding these theories would help to apply appropriate algorithms to ensure biomarker systems successful.The principles of key -computational approaches for selecting efficiently the best subset of biomarkers from high--dimensional omics data are highlighted.Appreciating these key advances would help to accelerate the development of clinically reliable biomarker systems.

View Article: PubMed Central - PubMed

Affiliation: Department of Surgery, Division of MultiOrgan Transplantation, University of California San Francisco , San Francisco, CA , USA.

ABSTRACT
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.

No MeSH data available.


Transplant fields require computations. The boxes show the areas of investigation needed by translational computational methods to advance organ transplant management. Figure adapted from Ref. (6).
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Figure 1: Transplant fields require computations. The boxes show the areas of investigation needed by translational computational methods to advance organ transplant management. Figure adapted from Ref. (6).

Mentions: Although short-term survival rates of grafts have increased, long-term graft survival rates have shown little improvement (2, 3). Five-year graft survival for transplanted organs varies from 43% for lung to 78% for kidney, highlighting the need for improved analysis of post-transplant injury pathways. There is a desperate urgency to advance the field of organ transplantation through improved monitoring by (a) the discovery of informative biomarkers, specific and sensitive to phenotypes of injury and acceptance, and (b) through improved algorithms and/or drugs for treatment with targeted efficacy and reduced toxicity (4, 5). Many single gene/protein pathway studies have shown associative and mechanistic insights into animal and restricted human sample studies, but the field has stalled with regard to the additional exponential insights needed at a genome-wide level to develop significant improvements in biomarker discovery for diagnosis/prediction and to evaluate the role of novel pathways for improved rational drug design as it applies to organ transplantation. In this review, we focus on the application of different computational approaches to mine high-dimensional human data in transplantation with a view to changing current clinical practice and patient management. Some of the critical requirements that the transplantation process needs to fulfill with this meta-data approach are highlighted in Figure 1.


Computational Models for Transplant Biomarker Discovery.

Wang A, Sarwal MM - Front Immunol (2015)

Transplant fields require computations. The boxes show the areas of investigation needed by translational computational methods to advance organ transplant management. Figure adapted from Ref. (6).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4561798&req=5

Figure 1: Transplant fields require computations. The boxes show the areas of investigation needed by translational computational methods to advance organ transplant management. Figure adapted from Ref. (6).
Mentions: Although short-term survival rates of grafts have increased, long-term graft survival rates have shown little improvement (2, 3). Five-year graft survival for transplanted organs varies from 43% for lung to 78% for kidney, highlighting the need for improved analysis of post-transplant injury pathways. There is a desperate urgency to advance the field of organ transplantation through improved monitoring by (a) the discovery of informative biomarkers, specific and sensitive to phenotypes of injury and acceptance, and (b) through improved algorithms and/or drugs for treatment with targeted efficacy and reduced toxicity (4, 5). Many single gene/protein pathway studies have shown associative and mechanistic insights into animal and restricted human sample studies, but the field has stalled with regard to the additional exponential insights needed at a genome-wide level to develop significant improvements in biomarker discovery for diagnosis/prediction and to evaluate the role of novel pathways for improved rational drug design as it applies to organ transplantation. In this review, we focus on the application of different computational approaches to mine high-dimensional human data in transplantation with a view to changing current clinical practice and patient management. Some of the critical requirements that the transplantation process needs to fulfill with this meta-data approach are highlighted in Figure 1.

Bottom Line: Understanding these theories would help to apply appropriate algorithms to ensure biomarker systems successful.The principles of key -computational approaches for selecting efficiently the best subset of biomarkers from high--dimensional omics data are highlighted.Appreciating these key advances would help to accelerate the development of clinically reliable biomarker systems.

View Article: PubMed Central - PubMed

Affiliation: Department of Surgery, Division of MultiOrgan Transplantation, University of California San Francisco , San Francisco, CA , USA.

ABSTRACT
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.

No MeSH data available.