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

The Availability Heuristic01:08

The Availability Heuristic

7.1K
A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
7.1K
The Representativeness Heuristic02:13

The Representativeness Heuristic

16.8K
The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
16.8K
The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

7.8K
In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
7.8K
Synthesis and Decomposition Reactions02:17

Synthesis and Decomposition Reactions

38.3K
Synthesis and decomposition are two types of redox reactions. Synthesis means to make something, whereas decomposition means to break something. The reactions are accompanied by chemical and energy changes. 
38.3K
Heuristics01:21

Heuristics

732
Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
732
Mean free path and Mean free time01:22

Mean free path and Mean free time

5.2K
Consider the gas molecules in a cylinder. They move in a random motion as they collide with each other and change speed and direction. The average of all the path lengths between collisions is known as the "mean free path."
5.2K

You might also read

Related Articles

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

Sort by
Same author

Augmenting transcriptome annotations through the lens of splicing evolution.

Genome research·2026
Same author

Data-driven AI system for learning how to run transcript assemblers.

Genome biology·2026
Same author

CodonMoE: DNA language models for codon-dependent mRNA prediction.

Bioinformatics (Oxford, England)·2026
Same author

Hash functions in nucleotide sequence analysis.

Genome research·2026
Same author

CodonRL: Multi-Objective Codon Sequence Optimization Using Demonstration-Guided Reinforcement Learning.

bioRxiv : the preprint server for biology·2026
Same author

seq2ribo: Structure-aware integration of machine learning and simulation to predict ribosome location profiles from RNA sequences.

bioRxiv : the preprint server for biology·2026
Same journal

circ2DGNN: circRNA-Disease Association Prediction via Transformer-Based Graph Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Hierarchical Hypergraph Learning in Association- Weighted Heterogeneous Network for miRNA- Disease Association Identification.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Discriminative Domain Adaption Network for Simultaneously Removing Batch Effects and Annotating Cell Types in Single-Cell RNA-Seq.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

MLW-BFECF: A Multi-Weighted Dynamic Cascade Forest Based on Bilinear Feature Extraction for Predicting the Stage of Kidney Renal Clear Cell Carcinoma on Multi-Modal Gene Data.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Generative Biomedical Event Extraction With Constrained Decoding Strategy.

IEEE/ACM transactions on computational biology and bioinformatics·2024
See all related articles

Related Experiment Video

Updated: Feb 8, 2026

Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway
11:25

Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway

Published on: March 7, 2022

5.3K

Theory and A Heuristic for the Minimum Path Flow Decomposition Problem.

Mingfu Shao, Carl Kingsford

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |July 11, 2018
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new heuristic for minimum path flow decomposition in directed acyclic graphs, significantly improving accuracy and efficiency for genome assembly and flow decomposition problems.

    More Related Videos

    Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
    06:52

    Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

    Published on: September 17, 2019

    6.8K
    Linking Predation Risk, Herbivore Physiological Stress and Microbial Decomposition of Plant Litter
    10:20

    Linking Predation Risk, Herbivore Physiological Stress and Microbial Decomposition of Plant Litter

    Published on: March 12, 2013

    14.0K

    Related Experiment Videos

    Last Updated: Feb 8, 2026

    Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway
    11:25

    Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway

    Published on: March 7, 2022

    5.3K
    Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
    06:52

    Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

    Published on: September 17, 2019

    6.8K
    Linking Predation Risk, Herbivore Physiological Stress and Microbial Decomposition of Plant Litter
    10:20

    Linking Predation Risk, Herbivore Physiological Stress and Microbial Decomposition of Plant Litter

    Published on: March 12, 2013

    14.0K

    Area of Science:

    • Computational Biology
    • Graph Theory
    • Algorithms

    Background:

    • Genome assembly and flow decomposition problems require efficient methods for analyzing network flows.
    • Existing algorithms for minimum path flow decomposition face limitations in accuracy and performance.

    Purpose of the Study:

    • To develop a novel, efficient heuristic for the minimum path flow decomposition problem in directed acyclic graphs.
    • To provide a theoretical framework for understanding the optimality gap in flow decomposition.

    Main Methods:

    • Developed fundamental theory connecting the optimality gap to nontrivial equations in flow values.
    • Designed a heuristic algorithm that iteratively reduces the gap by identifying these equations.
    • Introduced an operation on graph substructures to facilitate gap reduction without affecting optimality.

    Main Results:

    • The heuristic algorithm achieves high accuracy on simulated and splice graph instances.
    • The proposed method significantly outperforms existing state-of-the-art flow decomposition algorithms.
    • Theoretical insights reveal the factors determining the optimal number of paths.

    Conclusions:

    • The developed heuristic offers a powerful and efficient solution for minimum path flow decomposition.
    • This work advances the state-of-the-art in computational biology and algorithm design.
    • The algorithm's implementation is publicly available for broader application.