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Updated: May 15, 2026

Multi-Gene Single Nucleotide Polymorphism Detection in Gastric Cancer Based on Ion Semiconductor Sequencing Platform

Multi-Gene Single Nucleotide Polymorphism Detection in Gastric Cancer Based on Ion Semiconductor Sequencing Platform

Published on: May 10, 2024

An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer.

Eshmal Iman¹, Sohail Jabbar², Shabana Ramzan³

¹Department Computer Science, University of Engineering and Technology Taxila Pakistan, Rahim Yar Khan, Pakistan.

The American Journal of Pathology

|May 13, 2026

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This study introduces an integrated method for analyzing gene expression data, combining unsupervised clustering with supervised classification. This approach effectively identifies biological patterns and key genetic biomarkers for precision medicine.

Area of Science:

Genomics
Bioinformatics
Machine Learning

Background:

High-dimensional gene expression data presents challenges for traditional analysis.
Integrating unsupervised and supervised methods can reveal complex biological patterns.

Purpose of the Study:

To develop a novel integrative framework for analyzing high-dimensional gene expression data.
To bridge exploratory data analysis with predictive modeling for genomic insights.

Main Methods:

Utilized K-means clustering to stratify data into distinct groups.
Employed cluster assignments as pseudo-labels for training supervised classifiers (SVM, Random Forest, Ensemble).
Validated clustering using hierarchical clustering and DBSCAN, visualized with PCA.

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Published on: February 5, 2018

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Published on: September 18, 2020

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Last Updated: May 15, 2026

Multi-Gene Single Nucleotide Polymorphism Detection in Gastric Cancer Based on Ion Semiconductor Sequencing Platform

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Published on: May 10, 2024

Detection of a CDH1 Rare Transcript Variant in Fresh-frozen Gastric Cancer Tissues by Chip-based Digital PCR

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Published on: February 5, 2018

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Published on: September 18, 2020

Main Results:

Identified three distinct clusters with high separability and reliability.
Achieved high predictive accuracy using supervised machine learning models.
Highlighted key genetic determinants and potential biomarkers through feature importance analysis.

Conclusions:

The integrative framework offers a scalable and interpretable approach for complex genomic data.
Demonstrated utility in biomarker discovery, patient stratification, and precision medicine applications.
Advances genomic research by combining unsupervised and supervised learning strengths.