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

You might also read

Related Articles

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

Sort by
Same author

Tissue-aware elastic net decomposition reveals shared and lineage-specific drug response biomarkers.

bioRxiv : the preprint server for biology·2026
Same author

Context-dependent correlations mislead transcriptomic network inference in bulk and single-cell data.

bioRxiv : the preprint server for biology·2026
Same author

WayFindR: investigating feedback in biological pathways.

NAR genomics and bioinformatics·2026
Same author

Clustering Digestive Tract Tumors Using Transcriptomic and Mutation Data.

Cancers·2026
Same author

A shape-constrained regression and wild bootstrap framework for reproducible drug synergy testing.

bioRxiv : the preprint server for biology·2026
Same author

Human Papillomavirus Integration Induces Oncogenic Host Gene Fusions in Oropharyngeal Cancers.

Cancer discovery·2025

Related Experiment Video

Updated: Nov 15, 2025

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors
06:32

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

2.5K

Pattern recognition in lymphoid malignancies using CytoGPS and Mercator.

Zachary B Abrams1, Dwayne G Tally2, Lin Zhang3

  • 1Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA. Zachary.Abrams@osumc.edu.

BMC Bioinformatics
|March 2, 2021
PubMed
Summary

The Mercator R package identifies known and novel cytogenetic patterns in lymphoid malignancies. This advance in biomedical informatics aids understanding of leukemia and lymphoma genetics.

Keywords:
CytoGPSKaryotypeLymphoid malignanciesMercatorPattern recognition

More Related Videos

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

12.8K
DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma
09:58

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

Published on: June 6, 2025

728

Related Experiment Videos

Last Updated: Nov 15, 2025

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors
06:32

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

2.5K
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

12.8K
DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma
09:58

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

Published on: June 6, 2025

728

Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Genetics

Background:

  • Large-scale biomedical data analysis presents challenges like sparsity and heterogeneity.
  • Recent advances include the CytoGPS algorithm for transforming text-based karyotypes into binary models.
  • The Mercator R package was developed to process and visualize binary biomedical data, addressing these challenges.

Purpose of the Study:

  • To apply the Mercator R package and CytoGPS algorithm for multidimensional pattern recognition in hematologic malignancies.
  • To analyze a large dataset of karyotype samples from public sources.
  • To investigate cytogenetic patterns in lymphoid hematologic malignancies.

Main Methods:

  • Utilized the Mercator R package for processing and visualization of binary biomedical data.
  • Combined Mercator with the CytoGPS algorithm for pattern recognition.
  • Performed a large-scale study on 22,741 karyotype samples from the Mitelman database, covering 47 hematologic malignancies.

Main Results:

  • Successfully processed and visualized a large, multidimensional dataset of karyotype samples.
  • Identified significant cytogenetic patterns within lymphoid hematologic malignancies.
  • The analysis revealed both previously known and novel genetic patterns.

Conclusions:

  • Mercator effectively identifies cytogenetic patterns in lymphoid malignancies.
  • The findings enhance the understanding of the genetic basis of leukemias and lymphomas.
  • This work demonstrates the utility of Mercator in analyzing complex biomedical data for cancer research.