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

RNA-seq

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...

You might also read

Related Articles

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

Sort by

Same author

A Commentary on Dual Orphan Nuclear Receptor 4A1 (NR4A1) and NR4A2 Ligands.

Journal of cellular immunology·2026

Same author

<i>Bifidobacteria infantis</i> and human milk oligosaccharides have independent and synergistic effects on immune response and amino acid metabolism in germ-free mouse models.

mSystems·2026

Same author

Digit regeneration in mice is stimulated by sequential treatment with FGF2 and BMP2.

Nature communications·2026

Same author

QuantumXCT: Learning Interaction-Induced State Transformation in Cell-Cell Communication via Quantum Entanglement and Generative Modeling.

ArXiv·2026

Same author

Brewed Coffee and Its Components Act Through Orphan Nuclear Receptor 4A1 (NR4A1).

Nutrients·2026

Same author

Intranasal Human NSC-Derived EVs Therapy Can Restrain Inflammatory Microglial Transcriptome, and NLRP3 and cGAS-STING Signalling, in Aged Hippocampus.

Journal of extracellular vesicles·2026

Search research articles

Related Experiment Video

Updated: Mar 27, 2026

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Published on: February 2, 2024

Quantum annealing for enhanced feature selection in single-cell RNA sequencing data analysis.

Selim Romero^1,2,3, Shreyan Gupta^1,3, Victoria Gatlin^1,3

¹Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, USA.

Quantum Machine Intelligence

|March 26, 2026

Summary

Quantum annealing-powered QUBO enhances gene selection in single-cell RNA sequencing (scRNA-seq) data. This approach identifies critical genes for cell differentiation and drug resistance, improving data interpretation beyond traditional methods.

Keywords:

Feature selection Quadratic unconstrained binary optimization (QUBO)Quantum annealing Quantum computing ScRNA-seq

More Related Videos

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Author Spotlight: Vascular Tissue Dissociation and Exploring Single-Cell Subclusters for Targeted Therapy

Author Spotlight: Vascular Tissue Dissociation and Exploring Single-Cell Subclusters for Targeted Therapy

Published on: January 19, 2024

Related Experiment Videos

Last Updated: Mar 27, 2026

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Published on: February 2, 2024

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Author Spotlight: Vascular Tissue Dissociation and Exploring Single-Cell Subclusters for Targeted Therapy

Author Spotlight: Vascular Tissue Dissociation and Exploring Single-Cell Subclusters for Targeted Therapy

Published on: January 19, 2024

Area of Science:

Computational Biology
Quantum Computing
Genomics

Background:

Single-cell RNA sequencing (scRNA-seq) generates complex, high-dimensional data.
Identifying key genes (features) is crucial for understanding cellular states and processes.
Traditional feature selection methods face challenges with scRNA-seq data complexity and interpretability.

Purpose of the Study:

To implement quantum annealing-empowered quadratic unconstrained binary optimization (QUBO) for feature selection in scRNA-seq data.
To evaluate the effectiveness of QUBO in identifying biologically relevant genes.
To compare QUBO's performance against traditional feature selection techniques.

Main Methods:

Utilized quantum annealing to solve QUBO models for feature selection.
Applied the method to scRNA-seq datasets from human cell differentiation and anticancer drug resistance studies.
Analyzed gene expression patterns identified by QUBO.

Main Results:

QUBO feature selection successfully identified genes associated with critical cell state transitions.
Demonstrated effectiveness in pinpointing genes related to differentiation and drug resistance.
Quantum annealing-QUBO revealed complex gene expression patterns potentially overlooked by conventional methods.

Conclusions:

Quantum annealing-powered QUBO offers a powerful new approach for feature selection in scRNA-seq analysis.
This method enhances the identification and interpretation of key genes driving cellular processes.
The findings suggest quantum computing can significantly advance biological data analysis.