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

Clinical Trials01:16

Clinical Trials

9.3K
Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...
9.3K
Clinical Trials: Overview01:11

Clinical Trials: Overview

3.4K
Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
3.4K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

184
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
184
Hazard Ratio01:12

Hazard Ratio

261
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
261
Targeted Cancer Therapies02:57

Targeted Cancer Therapies

7.8K
The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against...
7.8K

You might also read

Related Articles

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

Sort by
Same author

Detecting Uncoded Self-Harm in Veterans' Electronic Health Records Using Positive and Unlabeled Learning: Retrospective Cohort Study.

Journal of medical Internet research·2026
Same author

Detecting Uncoded Self-Harm in Veterans' Electronic Health Records Using Positive and Unlabeled Learning: Retrospective Observational Study.

Journal of medical Internet research·2026
Same author

The Common Fund Data Ecosystem (CFDE).

bioRxiv : the preprint server for biology·2026
Same author

KG2ML: integrating knowledge graphs and positive unlabeled learning for identifying disease-associated genes.

Frontiers in bioinformatics·2026
Same author

Badapple 2.0: An Empirical Predictor of Compound Promiscuity, Updated, Modernized, and Enhanced for Explainability.

Journal of chemical information and modeling·2025
Same author

Gene-Expression Programs in Salivary Gland Adenoid Cystic Carcinoma Analyzed Using Single-Cell and Spatial Transcriptomics.

bioRxiv : the preprint server for biology·2025

Related Experiment Video

Updated: Sep 18, 2025

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.3K

TICTAC: target illumination clinical trial analytics with cheminformatics.

Jeremiah I Abok1, Jeremy S Edwards1, Jeremy J Yang2

  • 1Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, NM, United States.

Frontiers in Bioinformatics
|June 24, 2025
PubMed
Summary

We developed an open-source pipeline to identify and rank disease-target associations for drug discovery. This tool integrates clinical trial data, enhancing biological target prioritization and accelerating therapeutic development.

Keywords:
clinical trial datadisease-targetdrug discoveryhypothesis generationinference

More Related Videos

Pre-clinical Evaluation of Tyrosine Kinase Inhibitors for Treatment of Acute Leukemia
10:49

Pre-clinical Evaluation of Tyrosine Kinase Inhibitors for Treatment of Acute Leukemia

Published on: September 18, 2013

18.3K
Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts
10:27

Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts

Published on: July 25, 2020

7.4K

Related Experiment Videos

Last Updated: Sep 18, 2025

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.3K
Pre-clinical Evaluation of Tyrosine Kinase Inhibitors for Treatment of Acute Leukemia
10:49

Pre-clinical Evaluation of Tyrosine Kinase Inhibitors for Treatment of Acute Leukemia

Published on: September 18, 2013

18.3K
Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts
10:27

Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts

Published on: July 25, 2020

7.4K

Area of Science:

  • Computational Biology
  • Drug Discovery
  • Bioinformatics

Background:

  • Identifying disease-target associations is crucial for drug discovery and therapeutic development.
  • Clinical trial data offers valuable insights but suffers from quality and interpretability issues.
  • An integrated approach is needed to consolidate diverse evidence for prioritizing biological targets.

Purpose of the Study:

  • To develop a data integration and visualization pipeline for inferring and evaluating disease-target associations.
  • To enable exploration of diseases linked to drug targets and vice versa.
  • To provide a scalable, open-source solution for hypothesis generation in drug discovery.

Main Methods:

  • Integrated clinical trial data with standardized metadata.
  • Employed robust aggregation techniques to consolidate multivariate evidence from multiple studies.
  • Developed a scoring framework using aggregated statistical metrics (e.g., meanRank) to rank and filter associations.

Main Results:

  • Successfully evaluated disease-target associations by linking protein-coding genes to diseases.
  • Incorporated a confidence assessment method based on aggregated evidence.
  • Systematically ranked associations to streamline the identification and prioritization of biological targets.

Conclusions:

  • The developed pipeline offers a scalable solution for hypothesis generation, scoring, and ranking in drug discovery.
  • As an open-source tool with publicly available datasets, it is designed for ease of use.
  • Empowers scientists to make data-driven decisions for prioritizing biological targets and discovering novel therapeutics.