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

DNA Microarrays02:34

DNA Microarrays

16.8K
Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
16.8K

You might also read

Related Articles

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

Sort by
Same author

Wnt-dependent spatiotemporal reprogramming of bone marrow niches drives fibrosis.

HemaSphere·2026
Same author

An oncogenic KRAS-driven secretome involving TNFα promotes niche preparation prior to pancreatic cancer onset.

Molecular cancer·2026
Same author

PHLOWER leverages single-cell multimodal data to infer complex, multi-branching cell differentiation trajectories.

Nature methods·2025
Same author

PILOT-GM-VAE: patient-level analysis of single-cell disease atlas with optimal transport of Gaussian mixture variational autoencoders.

Briefings in bioinformatics·2025
Same author

Inhibiting the alarmin-driven hematopoiesis-stromal cell crosstalk in primary myelofibrosis ameliorates bone marrow fibrosis.

HemaSphere·2025
Same author

Advances and challenges in cell-cell communication inference: a comprehensive review of tools, resources, and future directions.

Briefings in bioinformatics·2025
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: May 4, 2026

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
13:19

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Published on: November 2, 2013

19.4K

Proximity measures for clustering gene expression microarray data: a validation methodology and a comparative

Pablo A Jaskowiak1, Ricardo J G B Campello1, Ivan G Costa2

  • 1University of São Paulo, São Carlos.

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|December 17, 2013
PubMed
Summary
This summary is machine-generated.

Choosing the right proximity measure is crucial for gene expression microarray data clustering. This study reveals that less common measures often outperform standard ones like Pearson, highlighting the need for scenario-specific selection in gene expression analysis.

More Related Videos

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

4.8K
Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.4K

Related Experiment Videos

Last Updated: May 4, 2026

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
13:19

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Published on: November 2, 2013

19.4K
Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

4.8K
Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.4K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Cluster analysis is a primary step for extracting insights from gene expression microarray data.
  • Selecting an appropriate proximity measure is critical for effective clustering, yet guidelines are lacking.
  • Pearson is widely used, but the performance of other measures remains largely unexamined.

Purpose of the Study:

  • To investigate the impact of different proximity measures on microarray data clustering.
  • To evaluate the performance of 16 proximity measures across diverse gene expression datasets.
  • To provide guidance on selecting proximity measures for specific experimental contexts.

Main Methods:

  • Evaluated 16 proximity measures on 52 gene expression microarray datasets from cancer and time-course experiments.
  • Developed a benchmark and a novel methodology, Intrinsic Biological Separation Ability (IBSA), for time-course data evaluation.
  • Compared the performance of commonly used measures (e.g., Pearson, Spearman, Euclidean distance) against less explored alternatives.

Main Results:

  • Measures not frequently used in gene expression literature demonstrated superior performance compared to standard measures.
  • The optimal proximity measure varied significantly between time-course and cancer gene expression data.
  • The IBSA methodology and benchmark offer a standardized approach for future research on time-course data.

Conclusions:

  • The choice of proximity measure for clustering gene expression data should be tailored to the specific experimental scenario (e.g., time-course vs. cancer).
  • Rarely used proximity measures can yield better clustering results than commonly employed ones.
  • The developed IBSA methodology and benchmark are valuable resources for advancing research in gene time-course data analysis.