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

Graphs of Functions01:30

Graphs of Functions

525
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...
525
First Derivatives and the Shape of a Graph01:22

First Derivatives and the Shape of a Graph

232
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...
232
Graphs of Equations in Two Variables01:30

Graphs of Equations in Two Variables

395
An equation with two variables, typically written in the form y = f(x) or Ax + By = C, describes a relationship between quantities represented by x and y. Each solution to such an equation is an ordered pair (x, y) that satisfies the equation when substituted. These pairs can be represented graphically to understand the variables' relationship visually.A common technique for constructing the graph of a two-variable equation is to create a value table. Begin by choosing several values for the...
395
Second Derivatives and the Shape of a Graph01:29

Second Derivatives and the Shape of a Graph

287
The second derivative of a function provides essential information about a graph's curvature and how it changes over an interval. It helps determine whether a function is concave upward or concave downward and identifies points where the curvature changes. These properties are fundamental in analyzing real-world scenarios, such as changes in road elevation, population growth, and economic trends.A function f(x) is considered concave upward on an interval if its graph lies above all its tangent...
287
Optimal Foraging00:48

Optimal Foraging

14.3K
How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
14.3K
What is Evolutionary History?02:35

What is Evolutionary History?

45.2K
Scientists record evolutionary history by analyzing fossil, morphological, and genetic data. The fossil record documents the history of life on Earth and provides evidence for evolution. However, both fossil and living organisms offer evidence that outlines Earth’s evolutionary history.
45.2K

You might also read

Related Articles

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

Sort by
Same author

A comparative analysis of topological domain callers over RNA-associated interactome.

BMC bioinformatics·2026
Same author

Analysis of chromatin structure reveals the connection between sQTLs and the splicing of distant genes.

Scientific reports·2025
Same author

GAT-HiC: Efficient Reconstruction of 3D Chromosome Structure via Residual Graph Attention Neural Networks.

IEEE transactions on computational biology and bioinformatics·2025
Same author

Joint analysis of sQTL and Hi-C reveals spatial proximity between sQTLs and target genes in cancer tissues.

Scientific reports·2025
Same author

The significance of chromosome conformation capture in 3D genome architecture comprehension.

Computational biology and chemistry·2025
Same author

DRGAT: Predicting Drug Responses Via Diffusion-Based Graph Attention Network.

Journal of computational biology : a journal of computational molecular cell biology·2024
Same journal

A Denoising Adversarial Model Based on Hyperellipsoidal Knowledge Representation Learning for DTI Prediction.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

Improving Cancer Driver Gene Prediction using Biological knowledge-guided Prompts for LLM.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

Exploring Complex Genetic Mechanisms in Brain Imaging Genetics via a New Multi-task Learning Method.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

A Multi-Modal Framework for Phage-Host Interaction Prediction Using Multi-View Contrastive Learning.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

Decoding Gene-Disease Associations with Computational Methods: A Survey.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

A Competitive Coevolution-based Cancer Driver Pathway Identification Algorithm for Maximizing Coverage, Mutual Exclusivity, and Subnet Importance.

IEEE transactions on computational biology and bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Apr 16, 2026

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke
05:30

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Published on: October 10, 2025

625

Optimal Reconstruction of Graph Evolution History Under Preferential Attachment Model.

Elena Kudret, Emre Sefer

    IEEE Transactions on Computational Biology and Bioinformatics
    |April 14, 2026
    PubMed
    Summary
    This summary is machine-generated.

    A new Integer Linear Programming approach (ILP-PA) reconstructs ancestral protein-protein interaction graphs more accurately than greedy methods. This method enhances understanding of biological network evolution and provides robust, biologically relevant solutions.

    More Related Videos

    Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
    09:32

    Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

    Published on: December 18, 2016

    13.1K
    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
    10:44

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

    Published on: December 7, 2021

    2.7K

    Related Experiment Videos

    Last Updated: Apr 16, 2026

    Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke
    05:30

    Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

    Published on: October 10, 2025

    625
    Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
    09:32

    Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

    Published on: December 18, 2016

    13.1K
    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
    10:44

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

    Published on: December 7, 2021

    2.7K

    Area of Science:

    • * Computational Biology
    • * Network Science
    • * Bioinformatics

    Background:

    • * Understanding biological network evolution is crucial for deciphering biomolecular functions.
    • * Protein-protein interaction (PPI) networks evolve dynamically, often modeled by graph growth principles like Preferential Attachment (PA).
    • * Existing PA-based ancestral graph reconstruction methods frequently use greedy algorithms, yielding suboptimal results.

    Purpose of the Study:

    • * To introduce ILP-PA, a novel Integer Linear Programming (ILP) approach for reconstructing historical PPI graphs.
    • * To maximize likelihood within the Preferential Attachment model for improved ancestral graph reconstruction.
    • * To enable analysis of near-optimal and multiple optimal solutions for diverse applications.

    Main Methods:

    • * Developed an Integer Linear Programming (ILP) formulation for ancestral PPI graph reconstruction under the PA model.
    • * Utilized heuristics from general-purpose ILP solvers to enhance the reconstruction process.
    • * Evaluated the ILP-PA approach on synthetic datasets and three real-world PPI networks (Commander complex, bZIP transcription factor family, herpesvirus).

    Main Results:

    • * ILP-PA solutions achieved higher likelihoods compared to existing techniques.
    • * Demonstrated superior robustness against model mismatches and data noise.
    • * Reconstructed ancestral graphs showed closer alignment with established biological findings across real datasets.

    Conclusions:

    • * ILP-PA offers a more accurate and robust method for reconstructing ancestral PPI networks within the PA model.
    • * The approach provides valuable insights into the evolutionary dynamics of biological networks.
    • * ILP-PA's ability to find multiple optimal solutions enhances its utility in biological research.