Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Modern Molecular Taxonomy

Modern Molecular Taxonomy

Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...

You might also read

Related Articles

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

Sort by

Same author

Intracortical BCI Performance is Robust to Changes in Attentional Load During Dual-Tasking.

bioRxiv : the preprint server for biology·2026

Same author

Expert-Level Detection of Epilepsy Markers in EEG on Short and Long Timescales.

NEJM AI·2025

Same author

Genomic and Transcriptomic Adaptation to Chlorhexidine in <i>Streptococcus</i> spp.

Journal of dental research·2025

Same author

Awareness of bone strength in patients with neuromuscular disorders: ERN EURO-NMD clinician survey and European patient survey.

Journal of the neurological sciences·2025

Same author

Building consensus on clinical outcome assessments for BCI devices. A summary of the 10th BCI society meeting 2023 workshop.

Journal of neural engineering·2024

Same author

Fecal Microbiota Transfer in Acute Graft-versus-Host Disease following Allogeneic Stem Cell Transplantation.

Visceral medicine·2024

Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026

Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026

Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026

Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026

Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026

Same journal

Primer Design through Submodular Function Estimation.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 15, 2026

Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow

Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow

Published on: June 14, 2017

Reference point insensitive molecular data analysis.

M Altenbuchinger¹, T Rehberg¹, H U Zacharias²

¹Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.

Bioinformatics (Oxford, England)

|September 17, 2016

Summary

This summary is machine-generated.

Molecular measurements in biomedicine rely on reference points, but discrepancies can affect statistical models. Zero-sum regression offers a reference-point-insensitive alternative, outperforming traditional methods like LASSO in simulations and real-world applications.

More Related Videos

Mapping Dysfunctional Protein-Protein Interactions in Disease

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Related Experiment Videos

Last Updated: Mar 15, 2026

Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow

Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow

Published on: June 14, 2017

Mapping Dysfunctional Protein-Protein Interactions in Disease

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Area of Science:

Biomedical data analysis
Statistical modeling
Bioinformatics

Background:

Biomedical molecular measurements require reference points (e.g., RNA, cells, biofluids).
Reference points are often chosen for convenience, not necessarily optimal biological relevance.
Discrepancies between measurement and biological reference points can distort data interpretation.

Purpose of the Study:

To investigate the statistical implications of reference point discrepancies in molecular measurements.
To propose and evaluate an alternative regression method robust to reference point choices.
To introduce a new algorithm for fitting zero-sum elastic nets.

Main Methods:

Analysis of regression model performance (LASSO) under reference point discrepancies.
Development and application of zero-sum regression as a reference-point-insensitive method.
Implementation of a coordinate descent algorithm for fitting zero-sum elastic nets.

Main Results:

Reference point discrepancies significantly compromise the performance of standard regression models like LASSO.
Zero-sum regression demonstrates superior performance compared to LASSO when reference points are poorly chosen.
The proposed method was validated through simulations and an integrated metabolomics-microbiome analysis.

Conclusions:

Reference point selection is a critical, often overlooked, factor in biomedical data analysis.
Zero-sum regression provides a robust framework for analyzing molecular data with inherent reference point challenges.
The developed R-package 'zeroSum' facilitates the application of this novel methodology.