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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.


Pubmed publications on transplant genomics and proteomics paper over last 10 years. Data were extracted from Pubmed by searching transplant and genomics or transplant and proteomics.
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Figure 2: Pubmed publications on transplant genomics and proteomics paper over last 10 years. Data were extracted from Pubmed by searching transplant and genomics or transplant and proteomics.

Mentions: Biological experimental tools that explore genome-wide profiling referred as “omics” provide promising pathways to investigate transplant biology, and they have been increasingly applied in transplantation, with the number of generated data tripling over last decade (Figure 2). These omics technologies (e.g., functional genomics for RNA analysis, proteomics for protein and peptide analysis, metabolomics for metabolite analysis, and antibiomics for HLA- and non-HLA-antibody analysis) also provide “big data” that contains high-dimensional variables. Harnessing the “big data” to low dimensional variables could generate small sets of biomarkers for diagnostic tools, which detect and predict transplant injury as well as discriminate different causes of injuries. However, these omics data are generally complex, due to its inherent high-dimensional complexity, platform differences, hybridization variations, and different data scales. These complexities challenge scientists to directly extract biologically valid and clinically useful information by selecting, generating, and using the appropriate computational tools to meet the demands of the composition of the input data.


Computational Models for Transplant Biomarker Discovery.

Wang A, Sarwal MM - Front Immunol (2015)

Pubmed publications on transplant genomics and proteomics paper over last 10 years. Data were extracted from Pubmed by searching transplant and genomics or transplant and proteomics.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 2: Pubmed publications on transplant genomics and proteomics paper over last 10 years. Data were extracted from Pubmed by searching transplant and genomics or transplant and proteomics.
Mentions: Biological experimental tools that explore genome-wide profiling referred as “omics” provide promising pathways to investigate transplant biology, and they have been increasingly applied in transplantation, with the number of generated data tripling over last decade (Figure 2). These omics technologies (e.g., functional genomics for RNA analysis, proteomics for protein and peptide analysis, metabolomics for metabolite analysis, and antibiomics for HLA- and non-HLA-antibody analysis) also provide “big data” that contains high-dimensional variables. Harnessing the “big data” to low dimensional variables could generate small sets of biomarkers for diagnostic tools, which detect and predict transplant injury as well as discriminate different causes of injuries. However, these omics data are generally complex, due to its inherent high-dimensional complexity, platform differences, hybridization variations, and different data scales. These complexities challenge scientists to directly extract biologically valid and clinically useful information by selecting, generating, and using the appropriate computational tools to meet the demands of the composition of the input data.

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.