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 microarray-based...

You might also read

Related Articles

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

Sort by

Same author

Trainable clustering framework for spatial transcriptomics.

Bioinformatics advances·2026

Same author

TextEconomizer: Enhancing lossy text compression with denoising transformers and entropy coding.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

The good, the bad, and the ugly: opportunities, challenges, and pitfalls in spatial proteomics modeling.

Briefings in bioinformatics·2026

Same author

Spatial information matters: are traditional imputation methods effective for spatial transcriptomics data?

Briefings in bioinformatics·2026

Same author

Polyp segmentation in colonoscopy images using DeepLabV3+.

Computers in biology and medicine·2025

Same author

CoDNet: controlled diffusion network for structure-based drug design.

Bioinformatics advances·2025

Search research articles

Related Experiment Video

Updated: Jul 26, 2026

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

PanClassif: Improving pan cancer classification of single cell RNA-seq gene expression data using machine learning.

Kazi Ferdous Mahin¹, Md Robiuddin¹, Mujahidul Islam¹

¹Department of Computer Science and Engineering, United International University, Plot-2, United City, Madani Avenue, Satarkul, Badda, Dhaka 1212, Bangladesh.

|January 9, 2022

Summary

PanClassif, a novel method using RNA-seq data, effectively detects and classifies cancer by identifying key genes. This approach enhances machine learning classifier performance for early cancer detection and treatment insights.

Keywords:

Cancer detection Classification Machine learning Single cell RNA-Seq Software package

More Related Videos

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Related Experiment Videos

Last Updated: Jul 26, 2026

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

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Cancer remains a leading cause of mortality worldwide.
Machine learning (ML) and RNA-sequencing (RNA-seq) are revolutionizing cancer research.
High-throughput sequencing data enables advanced cancer identification and classification.

Purpose of the Study:

To introduce PanClassif, a novel method for cancer detection and classification using minimal, effective genes from RNA-seq data.
To enhance the performance of various machine learning classifiers for cancer prediction.
To provide a robust tool for analyzing large cancer datasets.

Main Methods:

Utilized The Cancer Genome Atlas (TCGA) dataset comprising 8287 cancer and 680 normal samples across 22 cancer types.
Applied k-Nearest Neighbour (k-NN) smoothing to reduce noise in RNA-seq data.
Selected effective genes using Anova-based tests and balanced training data with SMOTE (Synthetic Minority Over-sampling Technique).

Main Results:

PanClassif demonstrated superior performance compared to existing state-of-the-art methods.
Achieved consistent results on single-cell RNA-seq datasets from Gene Expression Omnibus (GEO).
Significantly improved performance for both binary cancer prediction and multi-class cancer classification tasks.

Conclusions:

PanClassif offers a powerful and efficient approach for cancer detection and classification using RNA-seq data.
The method shows broad applicability across various machine learning models and cancer types.
PanClassif is available as an open-source Python package, facilitating further research and development.