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

Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...

You might also read

Related Articles

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

Sort by
Same author

Ancient DNA reveals pervasive directional selection across West Eurasia.

Nature·2026
Same author

Integrating 730,947 exome sequences with clinical literature improves gene discovery.

medRxiv : the preprint server for health sciences·2026
Same author

Functional dissection of complex trait variants at single-nucleotide resolution.

Nature·2026
Same author

Widespread naturally variable human exons aid genetic interpretation.

Nature communications·2025
Same author

Pan-UK Biobank genome-wide association analyses enhance discovery and resolution of ancestry-enriched effects.

Nature genetics·2025
Same author

Efficiently quantifying dependence in massive scientific datasets using InterDependence Scores.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same journal

A native sulfur deposit in Gale crater, Mars.

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

Coordinated demise of harmful algal blooms.

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

Genetic effects put into context.

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

Bacteria share proteins to survive antibiotics.

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

Impacts shaped Earth's first continents.

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

Erratum for the Report "Covalently bonded single-molecule junctions with stable and reversible photoswitched conductivity" by C. Jia <i>et al</i>.

Science (New York, N.Y.)·2026
See all related articles

Related Experiment Video

Updated: May 26, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Detecting novel associations in large data sets.

David N Reshef1, Yakir A Reshef, Hilary K Finucane

  • 1Department of Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. dnreshef@mit.edu

Science (New York, N.Y.)
|December 17, 2011
PubMed
Summary
This summary is machine-generated.

We introduce the Maximal Information Coefficient (MIC), a novel measure for finding variable relationships in large datasets. MIC identifies diverse associations, proving useful across various scientific fields.

More Related Videos

A User-friendly and Powerful R Analysis of Large-scale Datasets
10:56

A User-friendly and Powerful R Analysis of Large-scale Datasets

Published on: November 4, 2025

Related Experiment Videos

Last Updated: May 26, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

A User-friendly and Powerful R Analysis of Large-scale Datasets
10:56

A User-friendly and Powerful R Analysis of Large-scale Datasets

Published on: November 4, 2025

Area of Science:

  • Statistics
  • Data Mining
  • Bioinformatics

Background:

  • Identifying complex relationships between variables in large datasets is crucial for scientific discovery.
  • Existing methods may fail to capture the full spectrum of associations, including non-linear and non-functional ones.

Purpose of the Study:

  • To introduce a new statistical measure, the Maximal Information Coefficient (MIC), for quantifying two-variable relationships.
  • To demonstrate the utility of MIC and the broader Maximal Information-based Nonparametric EXploration (MINE) statistics across diverse datasets.

Main Methods:

  • The Maximal Information Coefficient (MIC) was developed as a measure of dependence for variable pairs.
  • MIC is part of the Maximal Information-based Nonparametric EXploration (MINE) suite of statistics.
  • The MIC and MINE statistics were applied to real-world datasets.

Main Results:

  • MIC effectively captures a wide array of associations, including functional and non-functional relationships.
  • For functional relationships, MIC scores approximate the coefficient of determination (R(2)).
  • Application to global health, gene expression, baseball, and microbiome data revealed known and novel relationships.

Conclusions:

  • MIC provides a powerful and versatile tool for exploring variable dependencies in large datasets.
  • The MINE framework offers a robust approach for data exploration and relationship discovery.
  • MIC and MINE have broad applicability in various scientific domains for identifying significant patterns.