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

Bar Graph01:07

Bar Graph

23.8K
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...
23.8K
Multiple Bar Graph01:07

Multiple Bar Graph

10.5K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
10.5K
Classification of Signals01:30

Classification of Signals

1.6K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Identifying novel Japanese heart failure variants via endothelial <i>cis</i>-regulatory element analysis.

Bioinformatics advances·2026
Same author

Reinforcement Learning-Driven Multiproperty Optimization in Molecular Design Using Multicontext Transcriptome Data.

Journal of chemical information and modeling·2026
Same author

Development and Validation of an m6A-Derived Prognostic Signature in Lung Adenocarcinoma.

Journal of Cancer·2026
Same author

Deep-learning-assisted scattering structured-illumination confocal microscopy for industrial super-resolution imaging.

Optics express·2026
Same author

Differences in reactive metabolite formation and cytochrome P450 binding between acetaminophen and its bicyclo[1.1.1]pentane bioisostere.

Drug metabolism and pharmacokinetics·2026
Same author

Simulation-guided chemical direct reprogramming informed by temporal cellular conversion processes at the single-cell level.

Communications chemistry·2026

Related Experiment Video

Updated: Mar 30, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K

Mining Discriminative Patterns from Graph Data with Multiple Labels and Its Application to Quantitative

Zheng Shao1, Yuya Hirayama1, Yoshihiro Yamanishi2,3

  • 1Department of Bioscience and Bioinformatics, Kyushu Institute of Technology , Iizuka, Fukuoka 820-8502, Japan.

Journal of Chemical Information and Modeling
|November 10, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new graph mining method for identifying discriminative subgraphs in multi-labeled data. The approach efficiently finds informative patterns for machine learning tasks like drug side effect prediction.

More Related Videos

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

1.0K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.5K

Related Experiment Videos

Last Updated: Mar 30, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K
Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

1.0K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.5K

Area of Science:

  • Machine learning and data mining
  • Bioinformatics and cheminformatics
  • Graph mining

Background:

  • Graph data is increasingly prevalent in machine learning and data mining.
  • Graph mining is crucial for extracting patterns from complex graph structures.
  • Efficiently collecting informative patterns from large graph datasets is a key challenge.

Purpose of the Study:

  • To develop a method for mining discriminative subgraphs from multi-labeled graph data.
  • To address applications in cheminformatics, such as predicting drug side effects and identifying ligand-target interactions.
  • To improve the efficiency and effectiveness of pattern extraction in graph mining.

Main Methods:

  • Proposed a novel approach for mining discriminative subgraphs.
  • Utilized multi-labeled graph data.
  • Validated the method on synthetic data and a real-world drug adverse effect prediction task.

Main Results:

  • The proposed method demonstrated effectiveness in identifying informative subgraph patterns.
  • Achieved superior performance compared to L1-norm logistic regression with standard fingerprints in drug adverse effect prediction.
  • Required a significantly smaller number of subgraph patterns to achieve high performance.

Conclusions:

  • The developed graph mining technique is effective for multi-labeled data.
  • The method offers a more efficient and performant alternative for cheminformatics applications.
  • Software for the proposed approach is publicly available.