Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

Inductive Reasoning

Inductive Reasoning

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...

Drug Discovery: Overview

Drug Discovery: Overview

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...

You might also read

Related Articles

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

Sort by

Same author

Enhancing the quality and trustworthiness of large language model-generated summaries of clinical oncology literature.

JAMIA open·2026

Same author

Supporting the working life exposome: Annotating occupational exposure for enhanced literature search.

PloS one·2024

Same author

Predicting potential target genes in molecular biology experiments using machine learning and multifaceted data sources.

iScience·2024

Same author

Knowledge-enhanced Graph Topic Transformer for Explainable Biomedical Text Summarization.

IEEE journal of biomedical and health informatics·2023

Same author

Large-scale neural biomedical entity linking with layer overwriting.

Journal of biomedical informatics·2023

Same author

Contextualized medication event extraction with striding NER and multi-turn QA.

Journal of biomedical informatics·2023

Search research articles

Related Experiment Video

Updated: May 31, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Discovering and visualizing indirect associations between biomedical concepts.

Yoshimasa Tsuruoka¹, Makoto Miwa, Kaisei Hamamoto

¹School of Information Science, Japan Advanced Institute of Science and Technology (JAIST), Nomi, Japan. tsuruoka@jaist.ac.jp

Bioinformatics (Oxford, England)

|June 21, 2011

Summary

This summary is machine-generated.

FACTA+ is a real-time text-mining system that finds and visualizes indirect associations between biomedical concepts. It aids researchers in discovering hidden relationships and understanding biomolecular events within scientific literature.

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Related Experiment Videos

Last Updated: May 31, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Area of Science:

Biomedical Informatics
Computational Biology
Text Mining

Background:

Discovering associations between biomedical concepts is crucial for understanding biological contexts.
Existing text-mining systems require enhancement for exploring diverse and hidden associations.
A user-friendly system is needed to facilitate the discovery and comprehension of biomedical relationships.

Purpose of the Study:

To describe FACTA+, a real-time text-mining system for identifying and visualizing indirect associations between biomedical concepts.
To enhance the discovery of hidden relationships and biomolecular events in biomedical literature.
To provide an intuitive platform for exploring concept associations and their significance.

Main Methods:

FACTA+ utilizes a machine learning model for detecting biomolecular events in text.
It employs co-occurrence statistics to uncover hidden associations between biomedical concepts.
The system incorporates visualization features to improve the interpretability of discovered associations.

Main Results:

FACTA+ enables real-time discovery and visualization of indirect associations between concepts like genes, diseases, and chemicals.
It identifies biomolecular events and hidden relationships, offering enhanced insights beyond traditional search engines.
The system provides a novel approach to exploring concept importance and categorization within MEDLINE abstracts.

Conclusions:

FACTA+ is the first real-time web application offering integrated detection of biomolecular events and visualization of indirect concept associations.
The system facilitates a more comprehensive understanding of biomedical literature by revealing complex relationships.
FACTA+ serves as a valuable tool for researchers in biomedical text-mining and knowledge discovery.