ReviewFrontiers in immunology2015
Computational Models for Transplant Biomarker Discovery.
Review in Frontiers in immunology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Identification of Candidate Biomarkers for Transplant Rejection from Transcriptome Data: A Systematic Review.Molecular diagnosis & therapy · 2019Pooled it
- Exploring Chronic Rejection in Organ Transplantation Through Computational Modeling.Results and problems in cell differentiation · 2026Review
- Article
- Molecular Markers of Kidney Transplantation Outcome: Current Omics Tools and Future Developments.International journal of molecular sciences · 2022Review
- FINET: Fast Inferring NETwork.BMC research notes · 2020Article
- Validation of systems biology derived molecular markers of renal donor organ status associated with long term allograft function.Scientific reports · 2018Article
- A combined biomarker and clinical panel for chronic graft versus host disease diagnosis.The journal of pathology. Clinical research · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.