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DNA Microarrays

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Updated: Sep 24, 2025

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

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A Machine Learning Method to Trace Cancer Primary Lesion Using Microarray-Based Gene Expression Data.

Qingfeng Lu¹, Fengxia Chen², Qianyue Li³

¹Oncology Department, Daqing Oilfield General Hospital, Daqing, China.

Frontiers in Oncology

|May 9, 2022

Summary

This summary is machine-generated.

Identifying the origin of cancer of unknown primary (CUP) is crucial for effective treatment. This study developed an efficient Extreme Gradient Boosting (XGBoost) method using gene expression data to accurately trace CUP

Keywords:

XGBoost cancer of the unknown primary site gene expression gene selection human malignancies

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Area of Science:

Oncology
Bioinformatics
Computational Biology

Background:

Cancer of unknown primary (CUP) constitutes 3%-5% of all malignancies.
Conventional methods often fail to identify the tissue of origin for CUP.
Accurate primary site identification is essential for improving patient prognosis through targeted therapy.

Purpose of the Study:

To develop an efficient computational method for tracing the primary site of CUP.
To leverage microarray-based gene expression data for CUP origin identification.
To improve clinical cancer traceability and patient outcomes.

Main Methods:

Utilized Extreme Gradient Boosting (XGBoost) algorithm for primary site prediction.
Employed large-scale gene expression datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO).
Trained and validated XGBoost models using a 4:1 ratio of training to independent testing data.

Main Results:

Achieved high 5-fold cross-validation accuracies of 96.9% (TCGA) and 95.3% (GEO).
Reached excellent macro-precision rates of 96.75% (TCGA) and 98.8% (GEO) on independent datasets.
Demonstrated the XGBoost framework's efficiency and potential for cost reduction in clinical cancer traceability.

Conclusions:

The developed XGBoost framework accurately identifies the primary site of CUP using gene expression data.
This method offers a highly efficient and potentially cost-effective tool for clinical cancer traceability.
The findings suggest significant utility for this approach in clinical cancer research and patient management.