Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

330
DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
330
Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

592
The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
592
Design Example: Traverse Angle Computations01:25

Design Example: Traverse Angle Computations

319
Traverse angle computations are a critical component of surveying, used to compute the internal angles within a closed traverse. A traverse consists of a series of connected lines forming a closed loop, often used for land boundary delineation or mapping. Calculating the internal angles ensures accuracy in the traverse geometry and is essential for checking survey data integrity.The process begins with known azimuths and bearings of the traverse sides. Internal angles at each vertex are...
319
Drug Discovery: Overview01:26

Drug Discovery: Overview

11.3K
Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
11.3K
Computed Tomography01:10

Computed Tomography

8.1K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
8.1K
Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

Blood Studies for Cardiovascular System I: Cardiac Biomarkers

840
Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
The essential diagnostic tools for detecting myocardial necrosis and monitoring individuals suspected of having acute coronary syndrome (ACS) include:
Troponins
Troponins, particularly cardiac troponins I and T, are the most precise and sensitive markers of myocardial injury. They are detectable within 4-6 hours of myocardial injury and remain...
840

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Not all reference samples are equal in single-cell transcriptomics of human kidney tissue.

JCI insight·2026
Same author

Phase I/II Trial of the FLT3 Kinase Inhibitor XY0206 in Patients With Relapsed/Refractory Acute Myeloid Leukemia.

European journal of haematology·2026
Same author

HLA and non-HLA antibody profiling in the urine of kidney transplant recipients.

Expert review of proteomics·2025
Same author

Cellular and Spatial Drivers of Unresolved Injury and Functional Decline in the Human Kidney.

bioRxiv : the preprint server for biology·2025
Same author

Leukocyte immunoglobulin like receptor B3 (LILRB3) and allograft survival: can precision medicine target health disparities?

Kidney international·2025
Same author

Efficacy and safety of cladribine, low-dose cytarabine and venetoclax in newly diagnosed and relapsed/refractory acute myeloid leukemia: results of a single center study.

Annals of hematology·2025

Related Experiment Video

Updated: Jan 27, 2026

Author Spotlight: Advancing the Analysis of Plasma Extracellular Vesicle Proteome for Cardiovascular Biomarker Studies
05:30

Author Spotlight: Advancing the Analysis of Plasma Extracellular Vesicle Proteome for Cardiovascular Biomarker Studies

Published on: January 31, 2025

1.1K

Computational Models for Transplant Biomarker Discovery.

Anyou Wang1, Minnie M Sarwal1

  • 1Department of Surgery, Division of MultiOrgan Transplantation, University of California San Francisco , San Francisco, CA , USA.

Frontiers in Immunology
|October 7, 2015
PubMed
Summary
This summary is machine-generated.

This review highlights computational theories and mathematical models crucial for developing reliable transplant biomarkers from omics data. Understanding these advances accelerates the discovery of new diagnostic and therapeutic tools in transplantation.

Keywords:
bioinformaticscomputationmodelrejectiontheorytransplant

More Related Videos

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
13:19

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Published on: November 2, 2013

17.1K
Biomarkers in an Animal Model for Revealing Neural, Hematologic, and Behavioral Correlates of PTSD
08:29

Biomarkers in an Animal Model for Revealing Neural, Hematologic, and Behavioral Correlates of PTSD

Published on: October 10, 2012

16.7K

Related Experiment Videos

Last Updated: Jan 27, 2026

Author Spotlight: Advancing the Analysis of Plasma Extracellular Vesicle Proteome for Cardiovascular Biomarker Studies
05:30

Author Spotlight: Advancing the Analysis of Plasma Extracellular Vesicle Proteome for Cardiovascular Biomarker Studies

Published on: January 31, 2025

1.1K
Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
13:19

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Published on: November 2, 2013

17.1K
Biomarkers in an Animal Model for Revealing Neural, Hematologic, and Behavioral Correlates of PTSD
08:29

Biomarkers in an Animal Model for Revealing Neural, Hematologic, and Behavioral Correlates of PTSD

Published on: October 10, 2012

16.7K

Area of Science:

  • Biomedical Research
  • Translational Medicine
  • Genomics and Proteomics

Background:

  • Transplantation faces persistent unmet diagnostic and therapeutic needs.
  • Omics technologies offer new avenues for biomarker discovery.
  • Computational approaches are essential for biomarker development.

Purpose of the Study:

  • To review key advances in theories and mathematical models for transplant biomarker development.
  • To discuss the advantages and limitations of these models.
  • To highlight computational approaches for biomarker selection from high-dimensional omics data.

Main Methods:

  • Review of theoretical advances in computational algorithms and mathematical models.
  • Discussion of principles for selecting biomarker subsets from omics data.
  • Introduction to prediction models and multi-microarray data integration.

Main Results:

  • Key theories and mathematical models relevant to transplant biomarker development are presented.
  • Computational approaches for efficient biomarker subset selection are highlighted.
  • Prediction models and data integration strategies are discussed.

Conclusions:

  • Understanding computational theories accelerates the development of clinically reliable transplant biomarker systems.
  • Appropriate application of algorithms ensures the success of biomarker systems.
  • Advances in omics and computational methods promise improved diagnostics and drug discovery in transplantation.