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

Optimal Foraging00:48

Optimal Foraging

14.2K
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.
14.2K
Methods of Medium Optimization01:28

Methods of Medium Optimization

1
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
1
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

390
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
390
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

4.5K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
4.5K
Absolute and Local Extreme Values01:22

Absolute and Local Extreme Values

130
The highest and lowest values of a function, relative to a reference axis, are known as extreme values. These include absolute maximum and absolute minimum values, which represent the highest and lowest points the function reaches across its entire domain. Within a restricted portion of the function, the highest and lowest values are referred to as local maximum and local minimum values, respectively.Periodic functions, such as sine and cosine, show extreme values at infinitely many points due...
130
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

8.8K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
8.8K

You might also read

Related Articles

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

Sort by
Same author

Advancing radiation oncology care in Ukraine during the war: impact of international observerships on professional development and clinical practice.

Frontiers in oncology·2026
Same author

Split-Dose FLASH Irradiation to Investigate the Clinical Feasibility of Multifield Treatments: The Effect of Split Dose and Dose Rate on FLASH.

International journal of radiation oncology, biology, physics·2026
Same author

Order-Dependent dissimilarity measures on phylogenetic trees.

Journal of mathematical biology·2026
Same author

A case study on SSD to SAD linear acceleartor calibration transition.

Journal of applied clinical medical physics·2025
Same author

Semi-automated topic identification for radiation oncology safety event reports using natural language processing and statistical modeling.

Medical physics·2025
Same author

AAPM task group report 288: Recommendations for guiding radiotherapy event narratives.

Medical physics·2024
Same journal

Effects of Seasonal Births and Predation on Disease Spread.

Bulletin of mathematical biology·2026
Same journal

Identifiability, Sensitivity, and Genetic Algorithms in Bacterial Biofilm Selection Models.

Bulletin of mathematical biology·2026
Same journal

Slow Evolution Towards Generalism in a Model of Variable Dietary Range.

Bulletin of mathematical biology·2026
Same journal

CBINN: Cancer Biology-Informed Neural Network for Unknown Parameter Estimation and Missing Physics Identification.

Bulletin of mathematical biology·2026
Same journal

A Cost-Sensitive Behavioral Modeling Analysis of the Early Identification and Control of Infectious Diseases.

Bulletin of mathematical biology·2026
Same journal

Tracking Dynamics of Superspreading Through Contacts, Exposures, and Transmissions in Edge-Based Network Epidemics.

Bulletin of mathematical biology·2026
See all related articles

Related Experiment Video

Updated: Mar 20, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K

Characterizing Local Optima for Maximum Parsimony.

Ellen Urheim1, Eric Ford2,3,4, Katherine St John5,6,7

  • 1Departments of Mathematics and Chemistry, Johns Hopkins University, Baltimore, MD, USA.

Bulletin of Mathematical Biology
|May 29, 2016
PubMed
Summary
This summary is machine-generated.

Finding the best phylogenetic tree is hard. Using subtree prune and regraft (SPR) metric for tree searches ensures finding the optimal tree, unlike nearest neighbor interchange (NNI).

Keywords:
Attraction basinsCompatibilityMaximum parsimonyPhylogenetic islandsTree searching

More Related Videos

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles
10:23

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

Published on: July 11, 2025

739
A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

36.3K

Related Experiment Videos

Last Updated: Mar 20, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K
A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles
10:23

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

Published on: July 11, 2025

739
A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

36.3K

Area of Science:

  • Computational Biology
  • Phylogenetics
  • Evolutionary Biology

Background:

  • Finding the optimal phylogenetic tree using maximum parsimony is computationally challenging.
  • Existing methods like nearest neighbor interchange (NNI) can get stuck in local optima, even with perfect data.

Purpose of the Study:

  • To quantify the occurrence of local optima in phylogenetic tree searches.
  • To compare the efficiency of different search metrics for finding global optima.

Main Methods:

  • Analysis of phylogenetic tree search spaces using nearest neighbor interchange (NNI) and subtree prune and regraft (SPR) metrics.
  • Simulation of sequence data under Cavender-Farris-Neyman (CFN) and Jukes-Cantor (JC) models of evolution.

Main Results:

  • NNI operations can lead to multiple local optima, preventing searches from reaching the global optimum, even for perfect sequence data.
  • The SPR metric guarantees a single local optimum for perfect data, ensuring rapid convergence to the global optimum.
  • Characterization of conditions under which simulated sequence data yield well-behaved search spaces.

Conclusions:

  • The choice of metric significantly impacts the efficiency and success rate of phylogenetic tree searches.
  • SPR metric offers a more reliable approach for finding the global optimum in maximum parsimony analyses.
  • Understanding search space properties is crucial for developing effective phylogenetic inference methods.