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

Ogive Graph01:07

Ogive Graph

6.7K
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...
6.7K
Graphing Antiderivatives01:30

Graphing Antiderivatives

53
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...
53
Bar Graph01:07

Bar Graph

21.5K
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...
21.5K
Time-Series Graph00:54

Time-Series Graph

5.0K
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...
5.0K
Multiple Bar Graph01:07

Multiple Bar Graph

9.0K
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...
9.0K
First Derivatives and the Shape of a Graph01:22

First Derivatives and the Shape of a Graph

70
In calculus, the concept of the first derivative plays a crucial role in understanding the behavior of a function over its domain. The first derivative, denoted as f’(x), provides insight into how a function changes at any given point, much like a cyclist adjusting speed along a winding trail. By analyzing the first derivative, mathematicians can determine where a function is increasing, decreasing, or reaching critical points.The first derivative provides a precise method for classifying...
70

You might also read

Related Articles

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

Sort by
Same author

Multi-Omics Characterization of Human Molecular Responses to Spaceflight Across Two Independent Missions.

bioRxiv : the preprint server for biology·2026
Same author

Interleukin 23 promotes a pro-inflammatory Th17 cell state by stabilizing RORγt and suppressing glucocorticoid receptor activity.

Immunity·2026
Same author

Preselection CD4+CD8+ thymocytes modulate TCR responsiveness following TCRβ selection.

Journal of immunology (Baltimore, Md. : 1950)·2026
Same author

Comprehensive Lineage Tracing Maps the Landscape of Cell Fate Decisions in Mouse Embryogenesis.

bioRxiv : the preprint server for biology·2026
Same author

Tree reconstruction guarantees from CRISPR-Cas9 lineage tracing data using Neighbor-Joining.

Genome research·2026
Same author

From alchemy to precision skeletal editing.

Science (New York, N.Y.)·2026
Same journal

Haplotype-aware long-read error correction.

Algorithms for molecular biology : AMB·2026
Same journal

Extension of partial atom-to-atom maps: uniqueness and algorithms.

Algorithms for molecular biology : AMB·2026
Same journal

Lossless pangenome indexing using tag arrays.

Algorithms for molecular biology : AMB·2026
Same journal

Dolphyin: a combinatorial algorithm for identifying 1-Dollo phylogenies in cancer.

Algorithms for molecular biology : AMB·2026
Same journal

Probing transcription factor subsets in gene regulatory networks.

Algorithms for molecular biology : AMB·2026
Same journal

Comparing the ability of embedding methods on metabolic hypergraphs for capturing taxonomy-based features.

Algorithms for molecular biology : AMB·2026
See all related articles

Related Experiment Video

Updated: Jan 27, 2026

HKUST-1 as a Heterogeneous Catalyst for the Synthesis of Vanillin
11:15

HKUST-1 as a Heterogeneous Catalyst for the Synthesis of Vanillin

Published on: July 23, 2016

10.7K

Connectivity problems on heterogeneous graphs.

Jimmy Wu1, Alex Khodaverdian2, Benjamin Weitz2

  • 11Department of Computer Science, Stanford University, Stanford, CA USA.

Algorithms for Molecular Biology : AMB
|March 23, 2019
PubMed
Summary
This summary is machine-generated.

We introduce the condition Steiner Network problem to find minimal subgraphs across multiple biological conditions. This problem is NP-hard to approximate, but improved algorithms exist for monotonic conditions, with a new solver developed for general instances.

Keywords:
Approximation algorithmNP hardProtein–protein interactionSteiner Network

More Related Videos

Measuring Deformability and Red Cell Heterogeneity in Blood by Ektacytometry
09:12

Measuring Deformability and Red Cell Heterogeneity in Blood by Ektacytometry

Published on: January 12, 2018

15.4K
Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
07:54

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Published on: October 25, 2011

19.1K

Related Experiment Videos

Last Updated: Jan 27, 2026

HKUST-1 as a Heterogeneous Catalyst for the Synthesis of Vanillin
11:15

HKUST-1 as a Heterogeneous Catalyst for the Synthesis of Vanillin

Published on: July 23, 2016

10.7K
Measuring Deformability and Red Cell Heterogeneity in Blood by Ektacytometry
09:12

Measuring Deformability and Red Cell Heterogeneity in Blood by Ektacytometry

Published on: January 12, 2018

15.4K
Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
07:54

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Published on: October 25, 2011

19.1K

Area of Science:

  • Computational Biology
  • Graph Theory
  • Network Analysis

Background:

  • Biological networks represent complex interactions, often studied using graph theory.
  • Extracting meaningful subgraphs from large biological networks is a key challenge.
  • Existing Steiner Network problems assume static reference graphs, which is insufficient for dynamic biological systems.

Purpose of the Study:

  • To introduce and analyze the "condition" Steiner Network problem, which accounts for multiple, distinct biological conditions.
  • To investigate the computational complexity and approximation limits of this new problem.
  • To develop practical solutions for analyzing dynamic biological networks.

Main Methods:

  • Formalized the "condition" Steiner Network problem with condition-specific demands and edge sets.
  • Proved NP-hardness and established approximation lower bounds for the general problem.
  • Developed an integer linear programming solver for practical applications.
  • Investigated approximation algorithms for monotonically growing graphs across conditions.

Main Results:

  • The "condition" Steiner Network problem is NP-hard to approximate within a factor of for C conditions.
  • Tight approximation bounds were established for the general case.
  • Improved approximation algorithms were demonstrated for monotonically changing networks.
  • The integer linear programming solver successfully found optimal solutions for human protein interaction network data.

Conclusions:

  • Accounting for multiple biological conditions significantly increases the complexity of network connectivity problems.
  • The developed solver provides a practical approach for analyzing dynamic biological networks.
  • Results offer theoretical guarantees for applications in multi-condition biological network analysis, extending to problems like Prize-Collecting Steiner Tree.