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primary topic: An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer

An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer
type: AcademicArticle
type: Article
abstract: This study presents a novel integrative approach for the analysis of high-dimensional gene expression data, leveraging the complementary strengths of unsupervised clustering and supervised classification. Using K-means clustering, the dataset is stratified into three distinct clusters, revealing intrinsic biological patterns and relationships. The resulting cluster assignments are subsequently employed as pseudo-labels to train machine learning models, including support vector machines, random forest, and a stacking ensemble classifier. To validate and enhance the robustness of clustering, complementary methodologies such as hierarchical clustering and DBSCAN are employed, with results visualized through PCA-driven dimensionality reduction. The high predictive accuracy achieved by the classifiers underscores the separability and reliability of the identified clusters. Furthermore, feature importance analysis highlighted key genetic determinants within each cluster, offering actionable insights into potential biomarkers and critical genomic features. This framework bridges the gap between exploratory unsupervised learning and predictive supervised modeling, providing a scalable and interpretable methodology for analyzing complex genomic datasets. Its applicability extends to biomarker discovery, patient stratification, and other precision medicine applications, emphasizing its utility in advancing genomic research and clinical practice.
date: 2026-05
is part of: The American Journal of Pathology
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
has document: An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer (Texto)
has document: An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer (Otro)
subject: Biomedicine
subject: Engineering

An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer (Texto)
type: Document
has file: PIIS0002944026001367.pdf
has part: PIIS0002944026001367.pdf

The American Journal of Pathology
type: Collection
issn: 00029440

Shabana Ramzan
type: Person
given name: Shabana
family name: Ramzan

Farwa Raoof
type: Person
given name: Farwa
family name: Raoof

Eshmal Iman
type: Person
given name: Eshmal
family name: Iman

Ali Raza
type: Person
given name: Ali
family name: Raza

Sohail Jabbar
type: Person
given name: Sohail
family name: Jabbar

Vivian Lipari
type: Person
given name: Vivian
family name: Lipari

Imran Ashraf
type: Person
given name: Imran
family name: Ashraf

Stefanía Carvajal-Altamiranda
type: Person
given name: Stefanía
family name: Carvajal-Altamiranda

An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer (Otro)
type: Document
is version of: An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer (Texto)
is volatile version of: An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer (Texto)
is index codes version of: An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer (Texto)
has file: indexcodes.txt
has part: indexcodes.txt

HTML Summary of #28577 An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer
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primary topic: An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer

Biomedicine
type: Concept
preferred label: Biomedicina @es

Engineering
type: Concept
preferred label: Ingeniería @es