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

Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Graphical Representation of Inequalities01:28

Graphical Representation of Inequalities

The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all points...
Causes of Similarity-Dissimilarity Effect01:26

Causes of Similarity-Dissimilarity Effect

The similarity-dissimilarity effect, a fundamental concept in social psychology, explains how interpersonal similarities and differences influence attraction and social interactions. This effect is supported by three key psychological perspectives: balance theory, social comparison theory, and consensual validation.Balance Theory and Cognitive ConsistencyBalance theory, developed by Fritz Heider, posits that individuals seek cognitive consistency in their relationships. When two people share...
Heuristics01:21

Heuristics

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...

You might also read

Related Articles

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

Sort by
Same author

Mechanisms for Anti-Inflammatory Activity of Gold Nanoparticles.

Combinatorial chemistry & high throughput screening·2026
Same author

Be the Human in the Loop: Guidance for Scientists in the Emerging Age of Co-Intelligence.

Combinatorial chemistry & high throughput screening·2025
Same author

Criteria and Protocol: Assessing Generative AI Efficacy in Perceiving EULAR 2019 Lupus Classification.

Diagnostics (Basel, Switzerland)·2025
Same author

Prediction of Lupus Classification Criteria via Generative AI Medical Record Profiling.

Biotech (Basel (Switzerland))·2025
Same author

Exploring the Blueprint of Life: The Innovation in Antibody and Protein Design.

Combinatorial chemistry & high throughput screening·2025
Same author

Combinatorial Paths to the Future: A Preface.

Combinatorial chemistry & high throughput screening·2025

Related Experiment Video

Updated: Jun 13, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

G-Hash: Towards Fast Kernel-based Similarity Search in Large Graph Databases.

Xiaohong Wang1, Aaron Smalter, Jun Huan

  • 1Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, USA.

Advances in Database Technology : Proceedings. International Conference on Extending Database Technology
|September 28, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces G-hash, a novel method for efficient graph similarity search in large databases. G-hash uses a kernel-based approach with hash tables for faster indexing and querying, outperforming existing methods in k-nearest neighbor classification.

Related Experiment Videos

Last Updated: Jun 13, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Area of Science:

  • Data Management
  • Graph Databases
  • Machine Learning

Background:

  • Graph databases present challenges for efficient storage, indexing, and similarity search.
  • Existing graph indexing methods often focus on subgraph matching, not similarity search.
  • Graph kernel functions offer similarity insights but suffer from high computational complexity and indexing difficulties.

Purpose of the Study:

  • To develop a novel kernel-based similarity measurement for graph data.
  • To propose an efficient indexing structure for graph data management.
  • To bridge the gap between graph kernel functions and efficient similarity search in graph databases.

Main Methods:

  • A novel kernel-based similarity measurement utilizing local node and neighbor features.
  • Implementation of a hash table for efficient storage and retrieval of local features.
  • Definition of a graph kernel function using hash tables for fast similarity querying, named G-hash.

Main Results:

  • G-hash achieves state-of-the-art performance in k-nearest neighbor (k-NN) classification on large chemical graph databases.
  • The G-hash method demonstrates scalability with smaller indexing size and faster indexing construction.
  • Query processing time is significantly improved compared to state-of-the-art methods like C-tree, gIndex, and GraphGrep.

Conclusions:

  • G-hash offers an efficient and scalable solution for graph similarity search.
  • The proposed method effectively utilizes local graph features for accurate similarity measurement.
  • G-hash significantly enhances performance for k-NN classification in graph databases.