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

Drug Discovery: Overview01:26

Drug Discovery: Overview

11.8K
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...
11.8K
Ogive Graph01:07

Ogive Graph

6.8K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
6.8K
Graphing Antiderivatives01:30

Graphing Antiderivatives

76
The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
76
Graphs of Functions01:30

Graphs of Functions

353
Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
353
Bar Graph01:07

Bar Graph

23.0K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
23.0K
Time-Series Graph00:54

Time-Series Graph

5.2K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
5.2K

You might also read

Related Articles

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

Sort by
Same author

Relation-aware pre-trained network with hierarchical aggregation mechanism for cold-start drug recommendation.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Enhanced immobilization of cadmium, lead, and antimony with improved soil fertility using sulfate-reducing bacteria@nano zero-valent iron-modified biochar: coupled chemisorption and microbial mechanisms.

Frontiers in microbiology·2026
Same author

CNER-Omni: A unified dynamic modality learning framework for Chinese named entity recognition across text and speech.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Data Augmentation for Few-Shot Biomedical NER Using ChatGPT.

Artificial intelligence in medicine·2025
Same author

Traits improvement of wild rice O. rufipogon via multiplex genome editing.

Journal of integrative plant biology·2025
Same author

ADENER: A syntax-augmented grid-tagging model for Adverse Drug Event extraction in social media.

Journal of biomedical informatics·2025

Related Experiment Video

Updated: Feb 9, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

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

Published on: June 13, 2025

1.6K

SemaTyP: a knowledge graph based literature mining method for drug discovery.

Shengtian Sang1, Zhihao Yang2, Lei Wang3

  • 1College of Computer Science and Technology, Dalian University of Technology, Hongling Road, Dalian, 116023, China.

BMC Bioinformatics
|May 31, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces SemaTyP, a novel biomedical knowledge graph approach to accelerate drug discovery by mining scientific literature. The method identifies potential new drug therapies and their mechanisms of action, complementing existing techniques.

Keywords:
Drug discoveryKnowledge graphLiterature miningLiterature-based discovery

More Related Videos

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

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

Published on: October 13, 2023

2.2K
Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products
11:13

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

Published on: March 12, 2020

11.6K

Related Experiment Videos

Last Updated: Feb 9, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

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

Published on: June 13, 2025

1.6K
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

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

Published on: October 13, 2023

2.2K
Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products
11:13

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

Published on: March 12, 2020

11.6K

Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Drug Discovery

Background:

  • Drug discovery is a lengthy and costly endeavor, despite advances in high-throughput screening and computer-aided methods.
  • Biomedical literature is a rich, yet underutilized, resource for identifying potential therapeutic targets and treatments.
  • Existing drug discovery pipelines face significant challenges in terms of time and expense.

Purpose of the Study:

  • To develop a novel method for drug discovery by leveraging biomedical knowledge graphs and literature mining.
  • To identify candidate drugs for diseases by analyzing relationships within published biomedical literature.
  • To provide a supplementary approach to current drug discovery methodologies.

Main Methods:

  • Construction of a biomedical knowledge graph by extracting relations from biomedical abstracts.
  • Training a logistic regression model based on semantic types of known drug therapy paths within the knowledge graph.
  • Application of the trained model to discover novel drug therapies for various diseases.

Main Results:

  • The proposed SemaTyP method effectively identifies new drug therapies for previously untreated diseases.
  • The approach provides insights into the potential mechanisms of action for candidate drugs.
  • Experimental validation demonstrates the efficacy of the knowledge graph-based literature mining approach.

Conclusions:

  • SemaTyP offers a novel knowledge graph-based literature mining strategy for drug discovery.
  • This method can serve as a valuable supplement to existing drug discovery techniques.
  • The approach enhances the identification of potential treatments by utilizing unstructured biomedical text data.